Exploring Six React Ecommerce Template Libraries

React is the most popular library for the development of e-commerce applications. React templates for building eCommerce apps are the combination of several components and elements. These small components are packed in containers and then they form the user interface of the React app. React templates for building eCommerce sites act as a blueprint for developers.

React eCommerce templates are dynamic and versatile and they can be used for building an exclusive React app for eCommerce. These templates are completely customizable and can take any shape as you specify them. As the front end of your React app will indicate the quality of your services. It is essential that you should design a scalable system to emancipate your business.

React eCommerce templates significantly improve your speed while React native app development services. You can find dedicated templates that are meant for providing value to your needs. Also, you do not need to make lots of changes to meet your requirements.

Why React Ecommerce Templates?

Ther React library has numerous templates for eCommerce development. While working in such templates you can modify each template according to your need independently. That means you are free to change any element on the UI without affecting other elements. This avoids the need for rendering the entire page every time you modify an element from the interface.

Every template in the react library is comprehensive and can be copied to another template That gives flexibility to a developer. Sometimes creating an interface for eCommerce sites becomes very confusing. In such cases, these templates act as a starting point or inspiration for development.

  Top React Ecommerce Templates

1. Molla

Molla is a fully-featured library for the development of excellent user interfaces and UX. This template library is loaded with a vast set of tools and features from which you can create an interface for the eCommerce app. Also with the help of this template the app developed can have amazing features and performance. This template provides you 20 sample eCommerce sites from which you can take reference for your ideas.

Molla is very convenient for getting a response and has a basic design architecture. It has a simple interface that is quick to understand and operate. It also supports full width strips layouts for the app. Molla provides access to a varied number of icons that are used in the eCommerce business. You can make your react app to display the latest products and services in an interactive manner.

This template can be used in several browsers and can have several animations or transitions for making the app interesting.

2. Novine

Novine is a simplified and interesting library that provides templates for eCommerce apps. Novine has enabled developers to use react.js, Next.js, React-Redux, ES6+, Sass as well as Bootstrap 4. This template library is used for the development of modern eCommerce sites that have excellent responsive capabilities. It has 4 demo react apps to understand the features.

Novine also has support in payment methods with the use of the latest strip methods. The documentation for this template is very detailed and includes everything that this template can do. Also, the components in this template provided by it are easy for modification. In addition to these features, there are a number of functionalities this template can provide you.

The source code of the template is simpler to handle and customize. It has support for a varied number of transitions and animations for every element you add to the app interface.

3. Livani

Livani is a clean and interactive template that is used for making scalable and modern react apps for eCommerce. This template is developed with the utility of Firebase Firestore, Firebase Auth, Express js, etc. it also includes stripe, and express js. Livani template offers a developer all the tools that are required for the development of eCommerce apps. Such apps developed from this template are capable of competing in the growing competition of several industries.

It has more than 5 sample apps, integration to payment methods, and also it is Retina Ready. The most exclusive feature is that the code of the Livani template is SEO optimized. You can also involve multiple fonts through google, interesting animation, and sliders for many actions. This liberty supports sliders and interactive text boxes for providing a responsive layout.

You can get the license of this template with a minimal fee of $ 29.

4. Lezada

With the use of Lezada, you can create extremely sleek and creative eCommerce sites. It is used for multipurpose website designs involving all the features at once in the app. This has all the high-quality features and functionalities that you may require for the Reactjs development services of a scalable eCommerce website. It supports more than 3 headers, 25+ sections ability, and more than 3 footer patterns.

Lezada is designed with the combination of HTML 5, Redux, react, WC3, and many more frameworks. It offers you to operate your website over multiple web browsers without a lack of performance. This is an excellent template for creating a website for product review and description because of the optimized functions of the temples. Lezada creates smart websites that are SEO optimized for the source code.

With all the features and capabilities Lezada is a feature-full template gallery for the development of eCommerce that can generate a majority of engagement.

5. Rick  

Rick is a reactJs template for developing eCommerce apps for mobile devices. This has excellent templates for the development of product selling apps for mobiles. You can use this template to create dynamic apps for selling accessories, digital products, and services. Along with this, the components in this template are made for customization. Hence you can modify the basic structure of the template according to your own requirements.

Rick has support for many google fonts and has cross-platform capabilities. You can create remarkable websites that can grow your business significantly. The structure of the template is very organized so you don’t get confused while making the changes. It too has SEO optimization and no cost updates on the templates. You can purchase the license for using the Rick template for just 24 USD.

 6. Multikart

Multikar is a popular template for designing an eCommerce app with the help of react. It has renowned for its use of creations of online stores for selling various things. This template has special optimization for using the eCommerce app in mobile devices. Also, this template is suggested for new enterprises because the development with their templates is very fast.

The major capability of Multikart is to make payments through Paypal and with other payment methods. It offers an authentication service that protects your website against bots and unauthorized users. If you are developing a large eCommerce app with a number of products this template can be your choice. This has an infinite scroll that can display all your products at once. Multikart supports several currencies.

 Final Verdict 

React templates for eCommerce apps make the job of development very straightforward. They provide the functionalities that can take hours for a developer to design. There is a wide range of tempters available for React that can be used for dedicated projects And the creation of noteworthy eCommerce apps. Mobile apps have become an important part of every business. Mobile apps have been affecting business for quite a while and help in expanding scalability. To develop an astonishing looking app with robust security and modern technology is a tough task. For this QuikieApps, the leading mobile app development company in Bangalore has the best expertise in mobile app development. To develop the finest applications with attractive interfaces and smooth operations, you can count on us.

The advancement of technology has positively influenced the growth of businesses all over the planet. With the help of modern technologies like websites and mobile applications, every firm can sell its products or services online without hassle. We, QuikieApps, have acquired recognition and reputation through the reliance of our respected clients as the top Web development company in Bangalore, India, USA, UK, Dubai. Adapting the dynamic technology of the web and mobile applications is the first step to success in this modish and competitive world.

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How AI Is Transforming Marketing for Better Conversions

Even when artificial intelligence (AI) is set to revolutionize the world, it’s clear that not all businesses maximize its potential. In the US, only 9% of companies use machine learning and voice recognition. And of the top 500 US firms, only 29% benefit from AI systems.

All these when the benefits of AI are loud and clear either in terms of monetary value and industry importance. Studies forecast that AI will likely grow to a whopping 17 trillion dollars in 10 years. Tech executives believe AI boosts people to be creative and productive. Plus, it generates more jobs. 

With continued research and innovation, AI will become something that no technology has ever seen. Some say it will become so accurate in predicting that we could control more of the future. Others believe AI will boost us into interstellar travel. While that might be a long shot, today’s AI — a blend of software and digital tools — is already changing the technological landscape. 

Artificial Intelligence as we know it today can mimic human intelligence and give predictive analysis based on billions of past data. And this is good news for almost all businesses. That means you can leverage AI more than human skills, which are prone to error and body limitations. 

Here are some of the best and widely used benefits of AI for businesses of all sizes: 

1. Automates Workflow

To automate and control repetitive, structured, and manual tasks — these are the main purposes of AI. When handled by humans, these tasks are prone to error. Even more, they induce chronic fatigue and restrict creativity among people. 

With AI, it’s possible to control repetitive tasks without human intervention. While AI is doing the work, humans can channel their effort on creativity and leadership work. 

Today’s digital tools are helpful when maneuvering workflow. For example, Timely, Toggl Track, Rescue Time, and Harvest are time-bound AI tools. Which means they help people keep track of time and schedules with ease. 

What’s more, is you can integrate these tools in team settings so that everyone can track time spent on each activity per person. They work in the background, so no one has to do something actively. 

Consider this: if you always take the menial and redundant task of keeping time stamps, chances are it robs you off of the ability to think and be creative. Instead, turn to AI timekeeping software for it does the job. 

Boomerang, Astro, AI Zimov are for those who spend hours on emails. These AI tools help you by reviewing and mimicking your email behavior. They filter messages that need the most immediate replies. Then, they copy your responses from past emails and use the data to respond to less important inboxes. 

For those looking to automate their workflow, AI tools such as Trello are top of the line. Their passive task-tracking ability helps people to break down project schedules and work files. And like Boomerang, these tools review recent behaviors on projects. Because of their machine learning capabilities, one word can trigger many suggestions.

The result: automated workflow prevents employee burnout. The ability to memorize human behavior helps AI tools to act almost independently. This leaves humans more time and energy to work on the creative, collaborative, and happier aspects. 

2. Gathers and Presents Data with Efficiency

Perhaps the most important advantage of AI is its ability to maneuver billions of data. An attribute no human can compare. Businesses nowadays capitalize on its efficiency and speed.

For instance, Deloitte created an AI program that can scan thousands of complicated legal documents. Pulling out and organizing textual information, this AI-powered tool speeds up contracts and other business negotiations.

On the other hand, chatbots are efficient responders. Not only for customer service, AI chatbots help companies distribute satisfaction surveys, collate data, and analyze them. Bots share data to HRs, too: leaves and incentives. Thanks to its analytics, a company could oversee bottlenecks, understaffed or overworked areas, high-cost departments, etc.

3. Automated Branding

Branding gorge on business’ budget. From logo to website design, everything is pricey. Hiring a designer or a design agency is a premium end not all companies can afford. 

But branding tools are getting smarter nowadays. Some logo creator tools such as Zyro, BrandCrowd logo maker, and Themeisle show pre-made logo templates you can customize to suit your brand’s goal image. 

Upon paying for the logo, these sites, for an additional price, provide automatic designs suited for different forms of marketing collaterals such as flyers, business cards, websites, t-shirts, even cups, and mugs. Imagine in a few clicks and cheaper prices, you’ll have a complete brand image. 

Of course, branding runs deeper than visual aspects. It’s the collective experience of all customers toward a business. Analytics tools such as Latana, Adobe Campaign, and Totango help brands feel and listen to customers’ perceptions. 

And that has been made easy with their AI-powered software. This means every transaction leads to analyzing customer behavior, segmenting them, and providing them a better experience next time. All these happen in the background even without human intervention. 

4. Sales Forecasting

The power of AI lies in its predictive technology. And one of the best ways it helps a company is through future forecasts, specifically about sales. 

Let’s accept it. The ultimate goal of business is to get better sales. With the help of AI, companies can calculate the future of sales even for the next few years. They do it by using billions of past data on market situations, brand following, trends, insights, etc. 

And this is insanely helpful for success. Not only can it know whether or not the brand will sell more. AI software provides strategic reports that can also guide the company on its next course of action. 

For example, predictive analytics tools such as those of IBM, SAB, and TIBCO help businesses formulate budgets, allocate resources, estimate potential revenue, and plan future growth. 

Specific uses of these AI tools: provides individual and team sales quotas to hit, a well-established process for salespeople to follow, and detailed customer relationship management (CRM) to track consumers along the sales funnel. 

5. Customer Targeting and Providing Better Algorithms

Social media rampantly use this form of technology. Every user knows this. Say you were searching for the best vlogging camera on Chrome for a set price. Then you log in on your Instagram, and on your feed come a selection of cameras, and this goes on across social media sites like Facebook, Twitter, Tiktok, you name it. which

Today’s social media hoards massive amounts of customers’ data. As a result, some advertising companies and businesses can generate custom-made content for a target audience using these data. 

This technology comprises a big chunk of advertising strategies. You can find these handy tools in CRM platforms, social media analytics, Google Adsense, etc. 

Fortunately, algorithms and customer targeting are not only to hook people to buy. It also helps brands to present unique and customized content for their target audience. For example, Twitter’s and TikTok’s AI gathers previous data so it can suggest things you would most likely engage in. 

Even reputable news sites such as the New York Times and Washington Post use customer-targeting tools. It can generate content based on your past interest. And for non-readers, they present stories that are either advertised or organically posted on their other paid channels. 

So there’s no reason why you shouldn’t adopt Artificial Intelligence. AI’s customer-centric tools are the key to acquiring more customers through targeted advertising strategies or giving more palatable services to your already trusting consumers.

Final Thoughts

AI doesn’t just directly improve a business. One indirect effect, nonetheless significant, is that AI relates to increasing work happiness and satisfaction. You can scour the entire literature on AI benefits, yet none is greater than that of happiness.

Happy people work the hardest. When AI takes over mundane and repetitive tasks, there’s more room for humans to breathe and think. As a result, they get to be more creative and productive, and happy. 

AI may not be in any businesses’ budget. Yet with its tremendous benefits and its projected growth years on, it’s not long before AI penetrates every market.   

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How IoT Will Be The Key To A More Personalized And Targeted Marketing

The Internet of things (IoT) is a fancy term for a connected world where almost everything man-made is connected to the internet. This phenomenon is not very full-blown these days. Farms don’t yet have a complete internet-connected irrigation system, and not all cities are laden with geographic sensors. 

But that’s not to say IoT won’t come to fruition. Our voice-assisted technologies like Alexa and Siri have become more personal and responsive in the last five years. Smartwatches get better at examining heart health and providing actionable care. Our voice can jumpstart a host of other mundane appliances like microwaves, TV, and air conditioners. 

While the IoT is not anywhere near full-scale adaptability, its power and importance cannot be understated. For one, tech companies have continually innovated products and services. Hence the industry will grow to more than 1,000 billion USD by 2027. Second, IoT improves the overall efficiency of consumers and the whole community in its continued improvement.

Consumers and cities are not the only ones who would benefit from internet-connected devices. So do marketing and advertising companies. But how? By leveraging the data that all those devices have collected, marketers can provide better content and present customized products and services to their target market. 

Here’s how marketers can benefit from the Internet of Things: 

1. Gather more data 

Let’s say you walked into your kitchen and placed a pre-cooked chicken in the microwave. You turned on the microwave and set the timer through your voice. When you entered your room, you told Alexa, Siri, or Cortana to activate the AC and the laptop and turn off all the lights. 

All these devices generate your data. The length of time you cook your meal, the time you wake up and turn on your AC, and the amount of energy you consume through different appliances. 

The amount of energy they generate, how often you exercise and what day of the week, and what time exactly, how long you leave the house, and what time you’re inside — all these are opportunities for massive amounts of data that no one can previously access. 

In the past decade, all the ways technologies get data is through the available technologies such as laptops, desktops, phones, tablets, and recently smartwatches and earphones. And yes they do good in knowing most about us. They can be nosy, actually. But the data they accumulate are very limited and sometimes not in line with reality. 

IoT enables marketers and other tech companies to look into someone’s daily life. Of course, these should adhere to security and data laws. If done right, marketers and tech companies will learn how consumers interact with the devices, how the devices improve their lives, and what needs improvement. 

So it’s a win-win situation. Again, it’s beneficial to consumers only if the tech companies and marketers handle the data responsibly and in accordance with data protection, laws, and regulations. 

2. Personalized content (better context) 

Imagine you don’t wake up until 11 am. But as early as 7 am, you get ads of slushies and healthy drinks, perhaps advertising companies thinking they could get you to buy morning drinks. Another scenario is when you get bombarded with ads of late-night meals and snacks when you sleep early in the evening. 

These are just a few instances where our current technologies and marketing strategies fall short. Some devices cannot detect what we are presently doing, hence they cannot provide accurate data to marketers. In turn, marketers cannot send customized and personalized content and advertisements for their targeted consumers. 

The Internet of Things and its hoards of connected smart devices, either at home or in the office, have access to data such as daily routines. They know the time you take your lunch or go to school. They know when you’re home when you’re out drinking, or where you’re at the office finishing work. 

Marketing tools can maneuver these beneficial data to both consumers’ and marketers’ advantage. Using the data, marketers can share better advertisements. Likewise, consumers can see personalized content. 

The best part is that both content and advertisements are time and context-sensitive. This means brands reach out to you at the right time and place, leveling up chances of engagement. Let’s accept it. You don’t want to receive ads on lunch when it’s 10 am. IoT data prevent that at all. 

3. Community building

IoT technologies are better community builders. People who use the same wearable device can form specific communities where they both share the benefits and challenges of the devices. 

For example, household X owns an Alexa at home. The kind of content Alexa generates for other people is more likely what’s generated for household X too. And as they get to share content based on these devices, they’ll form communities of like-minded people. 

Marketers can take this as an opportunity to tailor their content to such devices. This is why newer formats such as FAQs and question-like headlines and titles get chosen more than other traditional formats, aka articles. 

Suppose you mostly write long-form articles and not other IoT-optimized formats. In that case, you diminish your chances of getting top results from content generated by IoT devices like Apple watch, Google home devices, factory sensors, or shipping radars. 

So for a marketing strategy to work in IoT, digestible and easy-to-comprehend formats should make part of your creative arsenals. 

In short, IoT devices will make way for new ways of content creation. Ways wherein content are not necessarily in-depth, but more simple, easily understandable, and capable of getting picked up by voice searches. 

4. Conversational commerce

One of the keys to better customer-company relations is through conversational communication. It means when consumers ask, you answer—that simple. Consumers ask for help, you give them the exact support they need. 

For marketers, these need consideration because most of our content is not conversational. Of course, the quality and research is there but the way it is written does not directly answer human queries. IoT and its devices are beginning to change all that. 

Take, for instance, voice assistant devices such as Cortana, Alexa, and Siri. The content they provide is direct answers to human questions. The responses are short and straightforward. And in seconds, they provide the help you actually need. 

Content marketers can learn from this by creating content designed to answer questions or provide help. As long as they are both easy to understand when spoken out loud, chances are your content will make it to the top of those voice-assisted results. 

Specific questions start with why, how, what, and more are good formats to reach the top results. For advertisers, that also means getting past the flowery and inconvenient product description but concise and short words answering some people’s concerns about which your product or service can help. 

Again all this with the goal of having your content reach consumers. And consumers nowadays use voice-assisted AI technologies. If your content is tailored to get picked up by these technologies, you’ll achieve better conversions. 

5. Sustainable and cost-savings

IoT will deliver massive amounts of specific and personalized data. Marketers and big companies can use these data to target an audience that is more likely to buy and use their products and services. 

In this way, there’s no need for A/B testing or targeting a slightly different pool of people to succeed in marketing. With IoT, marketing strategies are laser focus. 

That will translate to a better bottom line. First, when the resources are allocated to specific targets, budgets are lessened but not at the expense of conversions. 

Much more important is that marketing strategies become more sustainable as it hinges on IoT. This marketing ability to transcend hundreds of billions of devices through just the Internet is much cheaper and sustainable. It doesn’t generate loads of electricity, money, and other resources. It only uses what’s widely available on the web and the data provided by the IoT tech producers. 

Final thoughts

AI has already proven itself to be worthy of attention and investment. For companies, It has leveraged the power of branding technologies such as AI logo makers or AI brand tracking. For consumers, it has allowed for a more customized experience. 

But all this can work better if only companies respect personal data privacy. Some companies suggest running data in encrypting technologies. But it may come a long way before everyone adopts such solutions. 

So for the rest of tech companies, it all boils down to responsible handling of data. Or else, the advertising industry will suffer a fatal blow. 

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Top Artificial Intelligence Trends for 2021

As a result of the pandemic, the use of artificial intelligence in many businesses has increased. By 2023, according to IDC, investment in AI technology would have increased to $97.9 billion. Artificial intelligence’s potential utility has only increased after the global COVID-19 epidemic. 

AI will become more essential as companies continue to automate day-to-day operations and better comprehend COVID-affected datasets. Businesses are more digitally connected than ever before since the lockdown and work-from-home policy was introduced.

When it comes to identifying the technologies that will revolutionize how we live, work, and play in the near future, AI is undeniably a hot topic. So, here’s a rundown of what we may expect in the coming year as we rebuild our lives and reassess our company plans and objectives.

We’ve witnessed first-hand how critical it is to swiftly evaluate and understand data on viral transmission throughout the world during this current outbreak. Governments, global health organizations, university research institutes, and businesses have joined together to develop new methods for collecting, aggregating, and working with data. We’ve become accustomed to watching the results of this on the news every night when the most recent infection or mortality statistics for our respective regions are announced. Enroll in an AI certification course and grab an AI certification to get started with the journey.

Trends in Artificial Intelligence (AI) to Watch in 2021

The objective of AI adoption is to increase operational efficiency or effectiveness. It can also be used to improve stakeholder satisfaction. Let’s look at the most crucial trends for the year 2021.

AI solutions for IT

AI solutions that can identify typical IT problems on their own and self-correct any minor faults or difficulties are expected to grow in popularity in the coming years. This will decrease downtime and allow teams to work on high-complexity projects while focusing on other things.

AIOps is becoming increasingly popular

The complexity of IT systems has risen in recent years. With AIOps solutions and enhanced analysis of the amounts of data coming their way, IT operations and other teams may improve their critical processes, decision-making, and duties. Forrester recommended IT executives seek for AIOps suppliers who can enable cross-team collaboration through end-to-end digital experiences, data correlation, and toolchain integration.

The data structure will be aided by AI

Organizations will make use of these technologies and generate data that can be used by RPA (robotic process automation) technology to automate transactional activities. RPA is one of the software industry’s fastest-growing segments. Its sole restriction is that it can only work with organized data. Unstructured data may be readily transformed into structured data with the aid of AI, resulting in a specified output.

Artificial intelligence talent will continue to be scarce

The lack of talent is anticipated to be a barrier to artificial intelligence deployment in 2021. There has been a chronic skills vacuum in AI, and businesses have finally recognized its promise. It is critical to close this gap and teach artificial intelligence to a larger number of individuals. In 2021, it will be critical to ensure that a wider range of users has access to artificial intelligence to focus on technology, learning techniques, and enabling a shift in the workplace.

AI is becoming widely adopted in the IT business

The usage of AI in the IT industry has been steadily increasing. On the other hand, some experts believe that businesses will begin to utilize AI in manufacturing and on a huge scale. An organization may achieve real-time ROI with the aid of artificial intelligence. This implies that organizations will reap the benefits of their work.

Augmented Processes have become increasingly popular

When it comes to innovation and automation in 2021, artificial intelligence and data science will be a component of a larger picture. Data ecosystems are scalable, lean, and provide timely data to a wide range of sources. However, in order to adapt and foster innovation, a foundation must be built. Artificial Intelligence may be used to improve software development processes, and we can expect for more collective intelligence and collaboration. To progress toward a long-term delivery strategy, we must cultivate a data-driven culture and go beyond the experimental stage.

Intelligence based on voice and language

The increase of remote working, particularly in customer care centers, has provided a huge potential for NLP and ASR (automated speech recognition) skills to be used. Because one-on-one tutoring isn’t available, businesses may employ artificial intelligence to do frequent quality checks on customer comprehension and intent to assure continuous compliance.

Emotional Artificial Intelligence

Because this technology can perceive, understand, and interact with different human emotions, emotional AI is one of the most popular Artificial Intelligence topics in 2021. Affective computing, as it is sometimes known, takes human-robot communication to a whole new level. Emotional AI can read both vocal and nonverbal signs to comprehend customer behaviour. By analysing how people react to particular topics, products, and services, high-tech cameras and chatbots can readily identify many sorts of human emotions. In the near future, this development in Artificial Intelligence will have a huge impact on the retail business.

Ethical AI

Some well-known businesses, such as Google, Microsoft, Apple, Facebook, and other digital behemoths, are developing ethical AI that adheres to a four-part ethical framework for successful data governance: fairness, accountability, transparency, and explainability. These firms are launching a slew of initiatives and studies in an effort to persuade other businesses to embrace ethical AI that is tailored to their specific needs.

Wrapping up

Over the next 18 months, we may expect more advancements in AI research that will improve our capacity to detect and respond to viral epidemics. This will, however, need continuing worldwide collaboration between governments and private businesses. Global politics and lawmakers, as well as the pace of technical progress, will very certainly influence how this plays out. As a result, concerns like access to medical databases and impediments to international information sharing will be major themes in the next year. artificial intelligence training is on the rise with people slowly understanding the importance and prospects in this industry. Getting an artificial intelligence certification is the sweetest deal in this hour. 

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Business Intelligence: Top Use Cases for the New-Age Businesses

Business intelligence has been making waves all over the world lately, especially in the business side of things. Organizations across all industries have it as a critical component for transformation towards becoming a data-driven enterprise. And, hence it is fueling the need for expanded investments to implement a successful BI strategy with tools, professionals & other training resources required.

Companies can use business intelligence to make a company’s raw data usable. They can help improve decision-making, strategic planning and other business functions. The goal is to make better decisions about your business by utilizing your data in more efficient and effective ways. Because BI has improved so much in the past decade, it’s much easier for more employees across the company to benefit from it. That’s because much of what makes up a business intelligence system is now streamlined or automated.

Are you wondering what that’s all about? Well, here are some of the reasons why it has gained popularity so rapidly among businesses across the broad spectrum of industries.

1. Better customer experiences: The key to success for any given business is its customers. The better you can understand your customers, the better you will be able to serve them and offer top-notch experiences. This, as anyone can tell, results in successful outcomes for the business. To that end, BI helps by providing extensive insights about customers to allow companies to adjust their offerings accordingly and thus deliver quality customer experiences.

2. Enhanced efficiency: Most businesses, no matter the industry they may be a part of, often struggle with efficiency and productivity issues across their operations at some point or the other. Business intelligence can contribute significant value in this regard as well, allowing the company to source data from all systems and analyze it to determine how effectively their processes and employees are performing. For example, in a hospital, it can be used to payroll systems, accounting systems, patient systems, etc. to effectively incentivize staff to achieve better efficiency levels.

3. Marketing: BI allows companies to fine-tune their marketing programs as well. It helps the relevant teams identify which of their campaigns offer the best results, what kind of marketing campaigns tend to work for their industry in particular, which social media platforms their target audience prefers, and countless other such questions and data points. Being able to understand such factors, in turn, empowers companies to better understand customers’ behavior, market trends and thus, adapt their marketing strategies to deliver better results.

4. Customized services: Easily one of the biggest benefits businesses stand to achieve from the use of BI is the ability to deliver personalized services to customers. This is a big win because tailored offerings are the biggest means to garner better sales. So, to help with that, business intelligence allows companies to take a closer look at their customers’ preferences, requirements, market trends, and other factors that are relevant to their industry. Such detailed insights, then, are put to work to develop customized content, products, messaging, etc. that is delivered to customers at the right time to improve chances of conversion and sales.

There is no denying the fact that the world today is brimming with new-age technologies, all of which offer to contribute unique value in some way or another. However, amid this crowd, business intelligence has made a mark. Enabling companies to keep an eye on the latest trends in the market, identify new technologies and important events, streamline their processes across the board, boost operational efficiency, drive better sales, achieve improved results and revenue, etc., business intelligence has quickly proven to be a valuable addition to the arsenal of any business that has had the foresight to embrace it.

What makes it even better is the fact that the benefits are achieved, no matter the industry. So, if you want to achieve all these benefits as well, we recommend you start looking ASAP for an expert vendor for business intelligence services & solutions.

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Orbits of Non-periodic Fourier Series: Simple Introduction, Cool Applications

These Fourier series can be considered as bivariate time series (X(t), Y(t)) where t is the time, X(t) is a weighted sum of cosine terms of arbitrary periods, and Y(t) is the same sum, except that cosine is replaced by sine. The orbit at time t is

where n can be finite or infinite, and Ak, Bk are the coefficients or weights. The shape of the orbit varies greatly depending on the coefficients: it can be periodic, smooth or chaotic, exhibits holes (or not), or fill dense areas of the plane. For instance, if Bk = k – 1, we are dealing with standard Fourier series, and the orbit is periodic. Also, X(t) and Y(t) can be viewed respectively as the real and imaginary part of a function taking values in the complex plane, as in one of the examples discussed here.

The goal of this article is to feature two interesting applications, focusing on exploratory analysis rather than advanced  mathematics, and to provide beautiful visualizations. There is no attempt at categorizing these orbits: this would be the subject of an entire book. Finally, a number of interesting, off-the-beaten-path exercises are provided, ranging from simple to very difficult.

The orbit is always symmetric with respect to the X-axis, since Y(-t) = –Y(t).

1. Application in astronomy

We are interested in the center of gravity (centroid) of n planets P1, …, Pn of various masses, rotating at various speeds, around a star located at the origin (0, 0), in a two-dimensional framework (the ecliptic plane). In this model, celestial bodies are assumed to be points, and gravitational forces between the planets are ignored. Also, for simplification, the orbit of each planet is circular rather than elliptic. Planet Pk has mass Mk, and its orbit is circular with radius Rk. Its rotation period is 2π / Bk. Also, at t = 0, all the planets are aligned on the X-axis.  Let M = M1 + … + Mn. Then the orbit of the centroid has the same formula as above, with Ak = Rk Mk / M for k = 1, …, n.

In the figures below, the left part represents the orbit of the centroid between t = 0 and t = 1,000 while the right part represents the orbit between t = 0 and t = 10,000.

Figure 1

Figure 2

Figure 3

In figure 1, we have n = 100 planets, all the planets have the same mass, Bk = k + 1, and Rk = 1 / (k + 1)^0.7 [ that is, 1 / (k + 1) at power 0.7]. The orbit is periodic because the Bk‘s are integers, though the period involves numerous little loops due to the large number of planets. The periodicity is masked by the thickness of the blue curve, but would be obvious to the naked eye on the right part of figure 1, if we only had 10 planets. I chose 100 planets because it creates a more beautiful, original plot.

Figure 2 is the same as figure 1, except that planet P50 has a mass 100 times bigger than all other planets. You would think that the orbit of the centroid should be close to the orbit of the dominant planet, and thus close to a circle. However this is not the case, and you need a much bigger “outlier planet” to get an orbit (for the centroid) close to a circle.

In figure 3, n = 50, Mk = 1 / SQRT(k+1), Ak = 1.75^(k+1), and Bk = log(k+1). This time, the orbit is non periodic. The area in blue on the right side becomes truly dense when t becomes infinite; it is not a visual effect. Note that in all our examples, there is  hole encompassing the origin. In many other examples (not shown here), there is no hole. Figure 3 is related to our discussion in section 2.

None of the above examples is realistic, as they violate both Kepler’s third law (see here) specifying the periods of the planets given Rk (thus determining Bk), and Titius-Bode law (see here) specifying the distances Rk between the star and its k-th planet. In other words, it applies either to a universe governed by laws other than gravity, or in the early process of planet formation when individual planet orbits are not yet in equilibrium. It would be an easy exercise to input the correct values of Ak and Bk corresponding to the solar system, and see the resulting non periodic orbit for the centroid of the planets.

2. The Riemann Hypothesis

The Riemann hypothesis is one of the most famous unsolved mathematical conjectures. It states that the Riemann Zeta function has no zero in a certain area of the (complex) plane, or in other words, that there is a hole around the origin in its orbit, depending on the parameter s, just like in Figures 1, 2 and 3. Its orbit corresponds to Ak = 1 / k^s, Bk = log k, and n infinite. Unfortunately, the cosine and sine series X(t), Y(t) diverge if s is equal to or less than 1. So in practice, instead of working with the Riemann Zeta function, one works with its sister called the Dirichlet Eta function, replacing X(t) and Y(t) by their alternating version, that is Ak = (-1)^(k+1) / k^s. Then we have convergence in the critical strip 0.5  <  s  <  1. Proving that there is a hole around the origin if 0.5  <  s  <  1 amounts to proving the Riemann Hypothesis. The non periodic orbit in question can be seen in this article as well as in figure 4.

Figure 4

Figure 4 shows the orbit, when n = 1,000. The right part seems to indicate that the orbit eventually fills the hole surrounding the origin, as t becomes large. However this is caused by using only n = 1,000 terms in the cosine and sine series. These series converge very slowly and in a chaotic way. Interestingly, if n = 4, there is a well defined hole, see figure 5. For larger values of n, the hole disappears, but it starts reappearing as n becomes very large, as shown in the left part of figure 4.

Figure 5

If n = 4 (corresponding to three planets in section 1 since the first term is constant here), a well defined hole appears, although it does not encompass the origin (see figure 5). Proving the existence of a non-vanishing hole encompassing the origin, regardless of how large t goes and regardless of s in ]0.5, 1[, when n is infinite, would prove the Riemann hypothesis. 

Note the resemblance between the left parts of figure 3 and 4. This could suggest two possible paths to proving the Riemann Hypothesis:

• Approximating the orbit of figure 4 by a an orbit like that of figure 3, and obtain a bound on the approximation error. If the bound is small enough, it will result in a smaller hole in figure 4, but possibly still large enough to encompass the origin.
• Find a topological mapping between the orbits of figure 3 and 4: one that preserves the existence of the hole, and preserves the fact that the hole encompasses the origin. 

 3. Exercises

Here are a few questions for further exploration. They are related to section 1.

• In section 1, all the planets are aligned when t = 0. Can this still happen again in the future if n = 3? What if n = 4? Assume that the orbit of the centroid is non periodic, and n is the number of planets.
• What are the conditions necessary and sufficient to make the orbit of the centroid non periodic?
• At the initial condition (t = 0), is the centroid always inside the limit domain of oscillations (the right part on each figure, colored in blue)? Or can the orbit permanently drift away from its location at t = 0, depending on the Ak‘s and Bk‘s?
• Find an orbit that has no hole. 
• Make a video, showing the planets moving around the star, as well as the orbital movement of the centroid of the planets. Make it interactive (like an API), allowing the users to input some parameters.
• Can you compute the shape of the hole is n = 3, and prove its existence?
• Try to categorize all possible orbits when n = 3 or n = 4.

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About the author:  Vincent Granville is a data science pioneer, mathematician, book author (Wiley), patent owner, former post-doc at Cambridge University, former VC-funded executive, with 20+ years of corporate experience including CNET, NBC, Visa, Wells Fargo, Microsoft, eBay. Vincent is also self-publisher at DataShaping.com, and founded and co-founded a few start-ups, including one with a successful exit (Data Science Central acquired by Tech Target). He recently opened Paris Restaurant, in Anacortes. You can access Vincent’s articles and books, here.

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Using Automation in AI with Recent Enterprise Tools

A Classic Data Science Project and approach looks like this:

Data Science (DS) and Machine Learning (ML) are the spines of today’s data-driven business decision-making.

From a human viewpoint, ML often consists of multiple phases: from gathering requirements and datasets to deploying a model, and to support human decision-making—we refer to these stages together as DS/ML Lifecycle. There are also various personas in the DS/ML team and these personas must coordinate across the lifecycle: stakeholders set requirements, data scientists define a plan, and data engineers and ML engineers support with data cleaning and model building. Later, stakeholders verify the model, and domain experts use model inferences in decision making, and so on. Throughout the lifecycle, refinements may be performed at various stages, as needed. It is such a complex and time-consuming activity that there are not enough DS/ML professionals to fill the job demands, and as much as 80% of their time is spent on low-level activities such as tweaking data or trying out various algorithmic options and model tuning. These two challenges — the dearth of data scientists, and time-consuming low-level activities — have stimulated AI researchers and system builders to explore an automated solution for DS/ML work: Automated Data Science (AutoML). Several AutoML algorithms and systems have been built to automate the various stages of the DS/ML lifecycle. For example, the ETL (extract/transform/load) task has been applied to the data readiness, pre-processing & cleaning stage, and has attracted research attention. Another heavily investigated stage is feature engineering, for which many new techniques have been developed such as deep feature synthesis, one-button machine, reinforcement learning-based exploration, and historical pattern learning.

However, such work often targets only a single stage of the DS/ML lifecycle. For example, AutoWEKA can automate the model building and training stage by automatically searching for the optimal algorithm and hyperparameter settings, but it offers no support for examining the training data quality, which is a critical step before the training starts. In recent years, a growing number of companies and research organizations have started to invest in driving automation across the full end-to-end AutoML system. For example, Google released its version of AutoML in 2018. Startups like H2O and Data Robot both introduced products. There are also Auto-sklearn and TPOT from the open-source community. Most of these systems aim to support end-to-end DS/ML automation. Dataiku became a leader in Enterprise AI tool which is giving us an all total new view too. There are many other platforms coming up.

Current capabilities are focused on the model building and data analysis stages, while little automation is offered for the human-labor-intensive and time-consuming data preparation or model runtime monitoring stages. Moreover, these works currently lack an analysis from the users’ perspective: Who are the potential users of envisioned full automation functionalities? Are they satisfied with the AutoML’s performance, if they have used it? Can they get what they want and trust the resulting models?

At the end of this article, we will see how Dataiku & other AI solutions or platforms can give us many solutions.

Data Science Team and Data Science Lifecycle:

Data science and machine learning are complex practices that require a team with interdisciplinary background and skills. For example, the team often includes stakeholders who have deep domain knowledge and own the problem; it also must have DS/ML professionals who can actively work with data and write code. Due to the interdisciplinary and complex nature of the DS/ML work, teams need to closely collaborate across different job roles, and the success of such collaboration directly impacts the DS/ML project’s final output model performance.

Data Science Automated:

 It often starts with the phases of requirement gathering & problem formulation, followed by data cleaning and engineering, model training and selection, model tuning and ensembles, and finally deployment and monitoring. Automated Data Science (AutoML) is the endeavor of automating each stage of this process separately or jointly. The Data cleaning stage focuses on improving data quality. It involves an array of tasks such as missing value imputation, duplicate removal, noise correction, invalid values, and other data collection errors. AlphaClean and HoloClean provide representative examples of automated data cleaning. Automation can be achieved through approaches like reinforcement learning, trial and error methodology, historical pattern learning, and more recently through knowledge graphs. The Hyperparameter selection stage is used to fine-tune a model or the sequence of steps in a model pipeline. Several automation strategies have been proposed, including grid search, random search, evolutionary algorithms, and finally sequential model-based optimization methods. AutoML has witnessed considerable progress in recent years, in research as well as application in commercial products. Various AutoML research efforts have moved beyond the automation on one specific step. Joint optimization, a type of Bayesian-Optimization-based algorithms, enables AutoML to automate multiple tasks together. For example, AutoWEKA , Auto-sklearn , and TPOT all automate the model selection, hyperparameter optimization, and assembling steps of the data science pipeline. The result coming out of such AutoML system is called a “model pipeline”. A model pipeline is not only about the model algorithm; it emphasizes the various data manipulation actions (e.g., filling in missing value) before the model algorithm is selected, and the multiple model improvement actions (e.g., optimize the best values for model’s hyperparameters) after the model algorithm is selected.

Amongst these advanced AutoML systems, Auto-sklearn and Auto-WEKA are two open-source efforts. Both use the sequential-parameter-optimization algorithm. This optimization approach generates model pipelines by selecting a combination of model algorithms, data pre-processors, and feature transformers. Their system architectures are both based on the same general-purpose-algorithm-configuration framework, SMAC (Sequential Model-based Optimization for General Algorithm Configuration). In applying SMAC, Auto-sklearn and Auto-WEKA translate the model selection problem into a configuration problem, where the selection of the algorithm itself is modeled as a configuration.

Auto-sklearn supports warm-starting the configuration search by trying to generalize configuration settings across data sets based on historic performance information. Leverage historical information to build a recommender system that can navigate the historical information more efficiently. This approach is effective in determining a pipeline but is also limited because it can only select from a pre-defined and limited set of pre-existing pipelines. To enable AutoML to dynamically generate pipelines instead of only selecting pre-existing pipelines were inspired by AlphaGo Zero and its pipeline generation algorithm. modeled as a single-player game. So the pipeline is built iteratively by selecting a set of actions (insertion, deletion, replacement) and a set of pipeline components (e.g., logarithmic transformation of a specific predictor or” feature”). Extend this idea to use a reinforcement learning approach, so that their final pipeline outcome is an ensemble of multiple sub-optimal pipelines, but that final pipeline has a state-of-the-art model performance when compared to other approaches. Model ensembles have become a mainstay in ML with all recent Kaggle competition-winning teams relying on them. Many AutoML systems generate a final output model pipeline as an ensemble of multiple model algorithms instead of a single algorithm. More specifically, the ensemble algorithm includes:

1) Ensemble selection, which is a greedy-search-based algorithm that starts with an empty set of models, incrementally adds a model to the working set and selects that model if such addition results in improving the predictive performance of the ensemble

2) And, genetic programming algorithm, which does not create an ensemble of multiple model algorithms, but it can compose derived model algorithms. An advanced version of the genetic programming algorithm that uses multi-objective genetic programming to evolve a set of accurate and diverse models via introducing bias into the fitness function accordingly.

Data Science Characters, Their Current and Favoured Levels of Automation, and Different Stages of Lifespan:

In this section, let’s take a closer look at data science workers’ current level of automation, and what their preferred level of automation would be in the future. The current and preferred levels of automation is also associated with different stages of the DS lifecycle. It is observed that there is a clear gap between the levels of automation in their current DS work practices and preferred automation in the future. Most respondents reported that their current work is at automation L0, which is “No automation, human performs the task”. Some participants reported L1 or even L2 levels of automation (i.e., “human-directed automation” and “system-suggested human-directed” respectively) in their current work practice, and these automation activities happened often in the more technical stages of the DS lifecycle (e.g., data pre-processing, feature engineering, modeling building, and model deployment). These findings echo the existing trend that AutoML system development and algorithm research work focus much more on the technical stages of the lifecycle. However, these degrees of automation are far less than what the respondents desired: participants reported that they prefer at least L1 automation across all stages, with the only exceptions in requirement gathering and model verification where a number of participants still prefer L0. The median across all the stages is L2 – human-guided automation.

In some of the stages, when asked about future preferences, a few respondents indicated that they want full automation (L4) over other automation levels. The Model deployment stage had the highest full automation preference, but it was still not the top choice of people. On average across stages, full automation was only preferred by 14% of respondents. Human-directed automation (L2) was preferred by most respondents (42%), while system-directed automation (L3) was the second preference (22%). This suggests that users of AutoML would always like to be informed and have control of the system to some degree. A full end-to-end automated DS lifecycle was not what people wanted. End-to-end AutoML systems should always have human-in-the-loop. There seems to be a trend in the results of the preferred levels of automation: in general, the desired levels of automation increases along with the lifecycle stages moving from less technical ones (e.g., requirement gathering) into the more technical ones (e.g., model building). L2 (System-suggested human-directed automation), L3 (system-directed automation), and L4 (full automation) are the levels when the human shifts some control and decision power to the system, and the AutoML system starts to take agency. And L2, L3, and L4 together took the majority vote of each stage. In summary, these results suggest that people definitely welcome more automation to help with their DS/ML projects, and there is a huge gap between what they use today and what they want tomorrow. However, people also do not want over-automated systems in human-centered tasks (i.e., requirement gathering, model verification, and decision optimization).

To have an in-depth examination of people’s preferred levels of automation, finer-grained level of automation preferences across different stages, and across different roles. It is worth noting that participants across all roles agreed that requirement gathering should remain a relatively manual process. Data scientists, both expert and citizen scientists, tend to be cautious about automation. Only a few of them expressed interest in fully automating (L4) feature engineering, model building, model verification, and runtime monitoring. For example, in model verification, they prefer system-suggested and human-directed (L3) and system-directed automation (L2) over too little automation (L1/L0) or too much automation (L4).

AI-Ops had a more conservative perspective toward automation than other roles, they only have a majority preference of full automation (L4) in the model deployment stage, some on data acquisition and data pre-processing, but for the rest stages, they would strongly prefer to have human involvement. Above all, there is a clear consensus among different roles that model deployment, feature engineering, and model building are the places where practitioners want higher levels of automation. This suggests an opportunity for researchers and system builders to prioritize automation work on these stages. On the other hand, all roles agree that less automation is desired in requirements gathering and decision optimization stages, this may be due to the fact these stages are currently labor-intensive human efforts, and it is difficult for our participants to even imagine how the automation would look like in these stages in the future.

Data Governance & Why?

Data governance is not a new idea – as long as data has been collected, companies have needed some level of policy and oversight for their management. Yet it largely stayed in the context, as businesses weren’t using data at a scale that required data governance to be top of mind. In the last few years, and certainly in the face of 2020’s tumultuous turn of events, data governance has shot to the forefront of discussions both in the media and in the boardroom as businesses take their first steps towards Enterprise AI. Recent increased government involvement in data privacy (e.g. GDPR and CCPA) has no doubt played a part, as have magnified focuses on AI risks and model maintenance in the face of the rapid development of machine learning. Companies are starting to realize that data governance has never really been established in a way to handle the massive shift toward democratized machine learning required in the age of AI. And that with AI comes new governance requirements. Today, the democratization of data science across the enterprise and tools that put data into the hands of the many and not just the elite few (like data scientists or even analysts) means that companies are using more data in more ways than ever before. And that’s super valuable; in fact, the businesses that have seen the most success in using data to drive the business take this approach. 

But it also gifts new challenges – mainly that businesses’ IT organizations are not able to handle the demands of data democratization, which has created a sort of power struggle between the two sides that slows down overall progression to Enterprise AI. A fundamental shift and organizational change into a new type of data governance, one that enables data use while also protecting the business from risk, is the answer to this challenge and the topic of this section.

Most enterprises today identify data governance as a very important part of their data strategy, but often, it’s because poor data governance is risky. And that’s not a bad reason to prioritize it; after all, complying with regulations and avoiding bad actors or security concerns is critical.   However, governance programs aren’t just beneficial because they keep the company safe – their effects are much wider: 

Governance isn’t about just keeping the company safe; data and AI governance are essential components to bringing the company up to today’s standards, turning data and AI systems into a fundamental organizational asset. As we’ll see in the next section, this includes wider use of data and democratization across the company.

AI Governance and Machine Learning Model Management?

Data governance traditionally includes the policies, roles, standards, and metrics to continuously improve the use of information that ultimately enables a company to achieve its business goals. Data governance ensures the quality and security of an organization’s data by clearly defining who is responsible for what data as well as what actions they can take (using what methods).

With the rise of data science, machine learning, and AI, the opportunities for leveraging the mass amounts of data at the company’s disposal have exploded, and it’s tempting to think that existing data governance strategies are sufficient to sustain this increased activity. Surely, it’s possible to get data to data scientists and analysts as quickly as possible via a data lake, and they can wrangle it to the needs of the business?

But this thinking is flawed; in fact, the need for data governance is greater than ever as organizations worldwide make more decisions with more data.  Companies without effective governance and quality controls at the top are effectively kicking the can down the road, so to speak, for the analysts, data scientists, and business users to deal with — repeatedly, and in inconsistent ways. This ultimately leads to a lack of trust at every stage of the data pipeline. If people across an organization do not trust the data, they can’t possibly confidently and accurately make the right decisions

IT organizations historically have addressed and been ultimately responsible for data governance. But as businesses move into the age of data democratization (where stewardship, access, and data ownership become larger questions), those  IT teams have often been put in the position incorrectly of also taking responsibility for information governance pieces that should really be owned by business teams. Because the skill sets for each of these governance components are different. Those responsible for data governance will have expertise in data architecture, privacy, integration, and modeling. However, those on the information governance side should be business experts — they know:

• What the data is?
• Where does the data come from?
• Is the source of the data correct?
• How valid this data is?
• How and why the data is valuable to the business?
• How the data can be used in different business contexts?
• What data security level is there?
• Has the business validated this data?
• Is it enough data?
• How the data ultimately should be used, which in turn, is the crux of a good Responsible AI strategy?

In brief, data governance needs to be teamwork between IT and business stakeholders.

Shifting from Traditional Data Governance to Data Science & AI Governance Model:

An old-style data governance program oversees a range of activities, including data security, reference and master data management, data quality, data architecture, and metadata management. Now with the growing adoption of data science, machine learning, and AI, there are new components that should also sit under the data governance. These are namely machine learning model management and Responsible AI governance. Just as the use of data is governed by a data governance program, the development and use of machine learning models in production require clear, unambiguous policies, roles, standards, and metrics. A robust machine learning model management program would aim to answer questions such as:

• Who is responsible for the performance and maintenance of production machine learning models?
• How are machine learning models updated and/or refreshed to account for model drift (deterioration in the model’s performance)?
• What performance metrics are measured when developing and selecting models, and what level of performance is acceptable to the business?
• How are models monitored over time to detect model deterioration or unexpected, anomalous data and predictions?
• How are models audited, and are they explainable to those outside of the team developing them?

It’s worth noting that machine learning model management will play an especially important role in AI governance strategies in 2020 and beyond as businesses leverage Enterprise AI to both recovers from and develop systems to better adapt to future economic change.

Responsible AI Governance and Keys to Defining a Successful AI Governance Strategy:

The second new aspect for a modern governance strategy is the oversight and policies around Responsible AI. While it has certainly been at the center of media attention as well as public debate, Responsible AI has also at the same time been somewhat overlooked when it comes to incorporating it concretely as part of governance programs.

Perhaps because data science is referred to as just that — a science — there is a perception among some that AI is intrinsically objective;  that is, that its recommendations, forecasts, or any other output of a machine learning model isn’t subject to individuals’ biases. If this were the case, then the question of responsibility would be irrelevant to AI – an algorithm would simply be an indisputable representation of reality.

This misconception is extremely dangerous not only because it is false, but also because it tends to create a false sense of comfort, diluting team and individual responsibility when it comes to AI projects. Governance around Responsible AI should aim to address this misconception, answering questions such as:

• What data is being chosen to train models, and does this data have a pre-existing bias in and of itself?
• What are the protected characteristics that should be omitted from the model training process (such as ethnicity, gender, age, religion, etc.)?
• How do we account for and mitigate model bias and unfairness against certain groups?
• How do we respect the data privacy of our customers, employees, users, and citizens?
• How long can we legitimately retain data beyond its original intended use?
• Are the means by which we collect and store data in line not only with regulatory standards but with our own company’s standards?

Following key Methods & Ethics to follow to define AI Governance Strategy:

A Top-Down and Bottom-Up Plan:

Every AI governance program needs executive sponsorship. Without strong support from leadership, it is unlikely a company will make the right changes (which — full transparency — are often difficult changes) to improve data security, quality, and management. At the same time, individual teams must take collective responsibility for the data they manage and the analysis they produce. There needs to be a culture of continuous improvement and ownership of data issues. This bottom-up approach can only be achieved in tandem with top-down communications and recognition of teams that have made real improvements and can serve as an example to the rest of the organization.

Stability Between Governance and Enablement:

Governance shouldn’t be a blocker to innovation; rather, it should enable and support innovation. That means in many cases, teams need to make distinctions between proofs-of-concept, self-service data initiatives, and industrialized data products, as well as the government, needs surrounding each. Space needs to be given for exploration and experimentation, but teams also need to make a clear decision about when self-service projects or proofs-of-concept should have the funding, testing, and assurance to become an industrialized, operationalized solution

Excellence at its Heart:

In many companies, data products produced by data science and business intelligence teams have not had the same commitment to quality as traditional software development (through movements such as extreme programming and software craftsmanship). In many ways, this arose because five to ten years ago, data science was still a relatively new discipline, and practitioners were mostly working in experimental environments, not pushing to production.  So, while data science used to be the wild west, today, its adoption and importance have grown so much that standards of quality applied to software development need to be reapplied. Not only does the quality of the data itself matter now more than ever, but also data products need to have the same high standards of quality — through code review, testing, and continuous integration/continuous development (CI/CD)  that traditional software does if the insights are to be trusted and adopted by the business at scale.

Model Organization:

As machine learning and deep learning models become more widespread in the decisions made across industries, model management is becoming a key factor in any AI Governance strategy. This is especially true today as the economic climate shifts, causing massive changes in underlying data and models that degrade or drift more quickly. Continuous monitoring, model refreshes, and testing are needed to ensure the performance of models meets the needs of the business. To this end, MLOps is an attempt to take the best of DevOps processes from software development and apply them to data science. 

Transparency and Accountable AI:

Even if, per the third component, data scientists write tidy code and adhere to high-quality standards, they are still giving away a certain level of control to complex algorithms. In other words, it’s not just about the quality of data or code, but making sure that models do what they’re intended to do.  There is growing scrutiny on decisions made by machine learning models, and rightly so.  Models are making decisions that impact many people’s lives every day, so understanding the implications of the decisions they make and making the models explainable is essential (both for the people impacted and the companies producing them). Open source toolkits such as Aequitas3, developed by the University of Chicago, make it simpler for machine learning developers, analysts, and policymakers to understand the types of bias that machine learning models bring.

 Data & AI Governance Weaknesses:

Data and AI governance aren’t easy; as mentioned in the introduction, these programs require coordination, discipline, and organizational change, all of which become even more challenging the larger the enterprise. What’s more, their success is a question not just of successful processes, but a transformation of people and technology as well. That is why despite the clear importance and tangible benefit of having an effective AI governance program, there are several pitfalls that organizations can fall into along the way that might hamper efforts:

A governance program without senior sponsorship means policies without “teeth,” so to speak. Data scientists, analysts, and business people will often revert to the status quo if there isn’t top-down castigation when data governance policies aren’t adhered to and recognition for when positive steps are taken to improve data governance.

If there isn’t a culture of ownership and commitment to improving the use and exploitation of data throughout the organization, it is very difficult for a data governance strategy to be effective. As the saying goes, “Culture eats strategy for breakfast.” Part of the answer often comes back to senior sponsorship as well as communication and tooling

A lack of clear and widespread communication around data governance policies, standards, roles, and metrics can lead to a data governance program being ineffective. If employees aren’t aware or educated around what the policies and standards are, then how can they do their best to implement them? 

Training and education are hugely important pieces of good data and AI governance.  It not only ensures that everyone is aware of policies but also can help explain practically why governance matters. Whether through webinars, e-learning, online documentation, mass emails, or videos, initial and continuing education should be a piece of the puzzle.

A centralized, controlled environment from which all data work happens makes data and AI governance infinitely simpler. Data science, machine learning, and AI platforms   can be a basis for this environment, and essential features include at a minimum contextualized documentation, a clear delineation between projects, task organization, change management, rollback, monitoring, and enterprise-level security

 Which solutions or platforms are Trending for Enterprise AI or specialist data scientists to make life easier in this area?

Not too hot, not too cold, but just right – these are the platforms that achieve a mix between being loved by techies and non-techies alike. This middle ground offers a strong focus on citizen data science users and heavy integration with programming languages, allowing for flexibility and in-platform collaboration between people who can code, and people who can’t. These platforms making life easy for data scientists in terms of executing a whole lengthy Data science projects steps and doing automation in the works.

Azure Machine Learning:

Microsoft is well known for seamlessly integrating their product offerings with each other, making Azure Machine Learning an attractive option for users who are already working in an existing Azure stack.  Azure Machine Learning’s main offering is the ability to build predictive models in-browser using a point-and-click GUI. Though the ability to write code directly in the platform is not available, specialized data scientists will be excited by Microsoft’s Python integration. The Azure ML library for Python allows users to normalize and transform data in Python themselves using familiar syntax, and call Azure Machine Learning models as needed using loops. Not only this, but Azure Machine Learning also integrates with existing Python ML packages (including scikit-learn, TensorFlow and PyTorch). For users familiar with these tools, distributed cloud resources can be used to productions results at scale, just like any other experiment. As of the writing of this article, Azure Machine Learning also offers an SDK for R in a public preview (i.e. non-productionisable) mode, which is expected to improve over time.

H2O Driverless AI:

H2O Driverless AI is the main commercial enterprise offering of the company H2O.ai, offering automated AI with some pretty in-depth algorithms, including advanced features like natural language processing. A strong focus on model interpretability gives users multiple options for visualizing algorithms in charts, decision trees, and flowcharts. H2O.ai is already well-known in the industry for its fully open-source ML platform H2O, which can be accessed as a package through existing languages like Python and R, or in notebook format. H2O Driverless AI and H2O currently exist as fairly separate products, though it is potential for these to be further integrated in the future. Partnerships with multiple cloud infrastructure providers (including AWS, Microsoft, Google Cloud, and Snowflake) make H2o Driverless AI a product to watch in the coming years.

DataRobot:

DataRobot offers a tool that is intended to empower business users to build predictive models through a streamlined point-and-click GUI. The tool focuses very heavily on model explainability, by generating flowcharts for data normalization and automated visuals for assessing model outcomes. These out-of-the-box visuals include important exploratory charts like ROC curves, confusion matrices, and feature impact charts. DataRobot’s end-to-end capabilities were significantly bolstered by the company’s acquisition of Paxata (a data preparation platform) in December 2019, which has since been integrated with the DataRobot predictive platform. The company also boasts some big-name partnerships, including Qlik, Tableau, Looker, Snowflake, AWS, and Alteryx. DataRobot does offer Python and R packages, which allow many of the service’s predictive features to be called through code, though the ability to directly write code in the DataRobot platform and collaborate with citizen data scientist users is not currently available (as of the writing of this article). DataRobot’s new MLOps service also provides the ability to deploy independent models written in Python/R (in addition to models developed in DataRobot), as part of a robust operations platform that includes deployment tests, integrated source control, and the ability to track model drift over time.

RapidMiner:

RapidMiner Studio is a drag & drop GUI-based tool for building predictive analytics solutions, with a free version providing analysis of up to 10,000 rows. In-database querying and processing are available through the GUI, but programmers/analysts also have the option to query in SQL code. The ETL process is handled by Turbo Prep, which offers a point & clicks data preparation (as well as direct export to .qvx, for users who want to import results into Qlik). The cool thing about RapidMiner is the integration with Python & R modules, available as supported extensions in the RapidMiner Marketplace, through which coders & non-coders can both collaborate on the same project. For coders working on a local Python instance, the RapidMiner library in Python also allows for the administration of projects and resources of a RapidMiner instance. For cloud-based scaling of models, RapidMiner also allows containerization using Docker and Kubernetes.

Alteryx:

An existing big player in the ETL tool market, Alteryx is used to build data transformation workflows in a GUI, replacing the need to write SQL code. Alteryx has significantly stepped up its game in recent years with its integrated data science offering, allowing users to build predictive models using their drag-and-drop “no-code” approach. The ability to visualize and troubleshoot results at every step of the operation is a huge plus, and users familiar with SQL should transition easily to the logical flowchart style of the ETL, removing the need for complex nested scripts. Alteryx has a fantastic online community with plenty of resources, and direct integration with both Python and R through out-of-the-box tools. The Python tool includes common data science packages such as pandas, scikit-learn, matplotlib, numpy, and others which will be familiar to the Python enthusiasts of this world.

Dataiku:

Dataiku is one of the world’s leading AI and machine learning platforms, supporting agility in organizations’ data eorts via collaborative, elastic, and responsible AI, all at enterprise scale. Hundreds of companies use Dataiku to underpin their essential business operations and ensure they stay relevant in a changing world. One quick look at the Dataiku website will make it immediately clear that this is a platform for everyone in the data space. Dataiku offers both a visual UI and a code-based platform for ML model development, along with a host of features that make Dataiku a highly sustainable platform in production. Data scientists will be delighted with not only the Python & R integration, but the flexibility in being able to code either using the embedded code editor, or their favorite IDE like Jupyter notebooks or RStudio. The Dataiku DSS (Data Science Studio) is available as an HTTP REST API, allowing users to manage models, pipelines, and automation externally. Data analysts will be excited by the multitude of plugins available – including PowerBI, Looker, Qlik. qvx export, Dropbox, Excel, Google Sheets, Google Drive, Google Cloud, OneDrive, SharePoint, Confluence, and many more. Automatic feature engineering, generation, and selection, in combination with the visual UI for model development, allows ML to sit firmly within the reach of these citizen data scientists.

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DSC Weekly Digest 17 August 2021

As data systems become more complex (and far-reaching), so too does the way that we build applications. On the one hand, enterprise data no longer just means the databases that a company owns, but increasingly refers to broad models where data is shared among multiple departments, is defined by subject matter experts, and is referenced not only by software programs but complex machine learning models.

The day where a software developer could arbitrarily create their own model to do one task very specifically seems to be slipping away in favor of standardized models that then need to be transformed into a final form before use. Extract, transform, load (ETL) has now given way to extract, load, transform (ELT). There’s even been a shift in best practices in the last couple of decades, with the idea that you want to move core data around as little as possible and rely instead upon increasingly sophisticated queries and transformation pipelines.

At the same time, the notion is growing that the database, in whatever incarnation it takes, is always somewhat local to the application domain. The edge is gaining in intelligence and memory, indeed, most databases are moving towards in-memory stores, and caching is evolving right along with them.

The future increasingly is about the query. For areas like machine learning, the query ultimately comes down to making models so that they are not only explainable, but tunable as well. The query response is becoming less and less about single the answer, and more about creating whole simulations.

At the same time, the hottest databases are increasingly graph databases that allow for inferencing, the surfacing of knowledge through the subtle interplay of known facts. Bayesian analysis (in various forms and flavors) has become a powerful tool for predicting the most likely scenarios, with queries here having to straddle the line between utility and meaningfulness. What happens when you combine the two? I expect this will be one of the hottest areas of development in the coming years.

SQL won’t be going away – the tabular data paradigm is still one of the easiest ways to aggregate data – but the world is more than just tables. A machine learning model, at the end of the day, is simply an index, albeit one where the keys are often complex objects, and the results are as well. A knowledge graph takes advantage of robust interconnections between the various things in the world and is able to harness that complexity, rather than get bogged down by it.

It is this that makes data science so interesting. For so long, we’ve been focused primarily on getting the right answers. Yet in the future, it’s likely that the real value of the evolution of data science is learning how to ask the right questions.

In media res,

Kurt Cagle
Community Editor,
Data Science Central

To subscribe to the DSC Newsletter, go to Data Science Central and become a member today. It’s free! 

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Top Use Cases of Natural Language Processing in Healthcare

Better access to data-driven technology as procured by healthcare organisations can enhance healthcare and expand business endorsements. But, it is not simple for the company enterprise systems to utilise the many gigabytes of health and web data. But, not to worry, the drivers of NLP in healthcare are a feasible part of the remedy.

What is NLP in Healthcare? 

The NLP illustrates the manners in which artificial intelligence policies gather and assess unstructured data from the language of humans to extract patterns, get the meaning and thus compose feedback. This is helping the healthcare industry to make the best use of unstructured data. This technology facilitates providers to automate the managerial job, invest more time in taking care of the patients, and enrich the patient’s experience using real-time data.

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You will be reading more in this article about the most effective uses and role of NLP in healthcare corporations, including benchmarking patient experience, review administration and sentiment analysis, dictation and the implications of EMR, and lastly the predictive analytics. 

14 Best Use Cases of NLP in Healthcare

Let us have a look at the 14 use cases associated with Natural Language Processing in Healthcare:

    1. Clinical Documentation

The NLP’s clinical documentation helps free clinicians from the laborious physical systems of EHRs and permits them to invest more time in the patient; this is how NLP can help doctors. Both speech-to-text dictation and formulated data entry have been a blessing. The Nuance and M*Modal consists of technology that functions in team and speech recognition technologies for getting structured data at the point of care and formalised vocabularies for future use

The NLP technologies bring out relevant data from speech recognition equipment which will considerably modify analytical data used to run VBC and PHM efforts. This has better outcomes for the clinicians. In upcoming times, it will apply NLP tools to various public data sets and social media to determine Social Determinants of Health (SDOH) and the usefulness of wellness-based policies.

    2. Speech Recognition

NLP has matured its use case in speech recognition over the years by allowing clinicians to transcribe notes for useful EHR data entry. Front-end speech recognition eliminates the task of physicians to dictate notes instead of having to sit at a point of care, while back-end technology works to detect and correct any errors in the transcription before passing it on for human proofing. 

The market is almost saturated with speech recognition technologies, but a few startups are disrupting the space with deep learning algorithms in mining applications, uncovering more extensive possibilities.

    3. Computer-Assisted Coding (CAC)

CAC captures data of procedures and treatments to grasp each possible code to maximise claims. It is one of the most popular uses of NLP, but unfortunately, its adoption rate is just 30%. It has enriched the speed of coding but fell short at accuracy.

    4. Data Mining Research

The integration of data mining in healthcare systems allows organizations to reduce the levels of subjectivity in decision-making and provide useful medical know-how. Once started, data mining can become a cyclic technology for knowledge discovery, which can help any HCO create a good business strategy to deliver better care to patients.

    5. Automated Registry Reporting

An NLP use case is to extract values as needed by each use case. Many health IT systems are burdened by regulatory reporting when measures such as ejection fraction are not stored as discrete values. For automated reporting, health systems will have to identify when an ejection fraction is documented as part of a note, and also save each value in a form that can be utilized by the organization’s analytics platform for automated registry reporting.

    6. Clinical Decision Support

The presence of NLP in Healthcare will strengthen clinical decision support. Nonetheless, solutions are formulated to bolster clinical decisions more acutely. There are some areas of processes, which require better strategies of supervision, e.g., medical errors. 

According to a report, recent research has indicated the beneficial use of NLP for computerised infection detection. Some leading vendors are M*Modal and IBM Watson Health for NLP-powered CDS. In addition, with the help of Isabel Healthcare, NLP is aiding clinicians in diagnosis and symptom checking. 

    7. Clinical Trial Matching

Using NLP and machines in healthcare for recognising patients for a clinical trial is a significant use case. Some companies are striving to answer the challenges in this area using Natural Language Processing in Healthcare engines for trial matching. With the latest growth, NLP can automate trial matching and make it a seamless procedure. 

One of the use cases of clinical trial matching is IBM Watson Health and Inspirata, which have devoted enormous resources to utilise NLP while supporting oncology trials.

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    8. Prior Authorization

Analysis has demonstrated that payer prior authorisation requirements on medical personnel are just increasing. These demands increase practice overhead and holdup care delivery. The problem of whether payers will approve and enact compensation might not be around after a while, thanks to NLP. IBM Watson and Anthem are already up with an NLP module used by the payer’s network for deducing prior authorisation promptly.

    9. AI Chatbots and Virtual Scribe

Although no such solution exists presently, the chances are high that speech recognition apps would help humans modify clinical documentation. The perfect device for this will be something like Amazon’s Alexa or Google’s Assistant. Microsoft and Google have tied up for the pursuit of this particular objective. Well, thus, it is safe to determine that Amazon and IBM will follow suit. 

Chatbots or Virtual Private assistants exist in a wide range in the current digital world, and the healthcare industry is not out of this. Presently, these assistants can capture symptoms and triage patients to the most suitable provider. New startups formulating chatbots comprise BRIGHT.MD, which has generated Smart Exam, “a virtual physician assistant” that utilises conversational NLP to gather personal health data and compare the information to evidence-based guidelines along with diagnostic suggestions for the provider.  

Another “virtual therapist” started by Woebot connects patients through Facebook messenger. According to a trial, it has gained success in lowering anxiety and depression in 82% of the college students who joined in.

    10. Risk Adjustment and Hierarchical Condition Categories

Hierarchical Condition Category coding, a risk adjustment model, was initially designed to predict the future care costs for patients. In value-based payment models, HCC coding will become increasingly prevalent. HCC relies on ICD-10 coding to assign risk scores to each patient. Natural language processing can help assign patients a risk factor and use their score to predict the costs of healthcare.

    11. Computational Phenotyping

In many ways, the NLP is altering clinical trial matching; it even had the possible chances to help clinicians with the complicatedness of phenotyping patients for examination. For example, NLP will permit phenotypes to be defined by the patients’ current conditions instead of the knowledge of professionals.

To assess speech patterns, it may use NLP that could validate to have diagnostic potential when it comes to neurocognitive damages, for example, Alzheimer’s, dementia, or other cardiovascular or psychological disorders. Many new companies are ensuing around this case, including BeyondVerbal, which united with Mayo Clinic for recognising vocal biomarkers for coronary artery disorders. In addition, Winterlight Labs is discovering unique linguistic patterns in the language of Alzheimer’s patients.

    12. Review Management & Sentiment Analysis

 NLP can also help healthcare organisations manage online reviews. It can gather and evaluate thousands of reviews on healthcare each day on 3rd party listings. In addition, NLP finds out PHI or Protected Health Information, profanity or further data related to HIPPA compliance. It can even rapidly examine human sentiments along with the context of their usage. 

Some systems can even monitor the voice of the customer in reviews; this helps the physician get a knowledge of how patients speak about their care and can better articulate with the use of shared vocabulary. Similarly, NLP can track customers’ attitudes by understanding positive and negative terms within the review. 

    13. Dictation and EMR Implications

On average, EMR lists between 50 and 150 MB per million records, whereas the average clinical note record is almost 150 times extensive. For this, many physicians are shifting from handwritten notes to voice notes that NLP systems can quickly analyse and add to EMR systems. By doing this, the physicians can commit more time to the quality of care. 

Much of the clinical notes are in amorphous form, but NLP can automatically examine those. In addition, it can extract details from diagnostic reports and physicians’ letters, ensuring that each critical information has been uploaded to the patient’s health profile. 

    14. Root Cause Analysis

Another exciting benefit of NLP is how predictive analysis can give the solution to prevalent health problems. Applied to NLP, vast caches of digital medical records can assist in recognising subsets of geographic regions, racial groups, or other various population sectors which confront different types of health discrepancies. The current administrative database cannot analyse socio-cultural impacts on health at such a large scale, but NLP has given way to additional exploration. 

In the same way, NLP systems are used to assess unstructured response and know the root cause of patients’ difficulties or poor outcomes.

This article was originally published here

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Using Automated Builds in ModelOps

In this installment of the ModelOps Blog Series, we will transition from what it takes to build AI models to the process of deploying into production. Think of this as the on ramp for extracting value from your AI investments—moving your model out of the lab and into an environment where it can provide new insights for your organization or add value to customers.

Front and center is the concept of continuous integration (CI) and continuous deployment (CD). This methodology can be applied to automate the process of releasing AI models in a reproducible and reliable manner. Get ready to walk away with everything you need to know in order to leverage containers to formalize and manage AI models within your organization.

The starting point for the deployment process is a source-control, versioned AI model. Need a refresher on how to get to the starting point? Review the previous blogs in this series which cover how to produce a model with responsibly sourced data and software development best-practices around model training and versioning.

Data Preparation: Putting the Right Process in Place for AI
Model Training: Our Favorite Tools in the Shed
Model Versioning. Reduce Friction. Create Stability. Automate.

Living in a containerized world

For ModelOps, containers are a standard way to package AI models to leverage in production. In essence, a container is a running software application comprised of the minimum requirements necessary to run the application, including an operating system, application source code, system dependencies, programming language libraries, and runtime. Containers are comprised of static container images that outline each resource and instruction required to bring the application to life within the container.

Your organization might already embrace containers or microservices in more traditional software and DevOps settings. But did you know containers can also be applied to the packaging and distribution of AI models for data science teams? That’s good news for leaders investing in the development of AI models because it means that models—and their difficult to install dependencies—can be packaged up into containers that can run anywhere. Upskilling and familiarizing your data science team with container technology will empower them to easily package their own AI models and participate in a robust CI/CD process—which can reduce your timeline to realize return on your AI investments.

Extending the notion of an AI model

Modzy extends the container concept to power AI models running in production. AI models are deployed through an open, standardized template that encourages developers to expose the functionality of their AI model while ensuring it can run anywhere (see example.) Keeping the focus on production deployment, a single set of best practices can be put into place. Without standardization, model developers often work in disparate development environments creating challenges with reproducing or handing off models from the research team to the production team.

Standardizing the process for how models are packaged ensures data scientists don’t need deep expertise in either software engineering or DevOps. However, they can reap the benefits of these disciplines. Data scientists can focus on developing new models to solve important problems instead of hacking together patchwork solutions every time a model is ready for deployment.

Ideally, you want a suite of standard templates for popular machine learning frameworks such as TensorFlow and PyTorch, giving data scientists the flexibility to use their tools of choice. This is a capstone to the process of model training described in Model Training: Our Favorite Tools in the Shed. Developers can make individualized decisions during the development of each model without compromising a streamlined process for model development and release.

Leveraging CI for automated builds

A CI/CD process that takes source code for a freshly developed AI model and automatically produces a containerized version of that model is the gold standard for build automation practices. Establishing such a process means that deployment is fully reproducible with no manually curated steps that could introduce error and consume valuable developer time. Modern CI frameworks such as Jenkins, CircleCI, or GitHub Actions are essential tools in the CI/CD pipeline. They keep your team’s development velocity high by allowing your data scientists to focus on developing their models instead of solving complicated deployment nuances—translating directly to an accelerated completion.

Modzy’s approach combines continuous integration best practices with containerization to build container images for models. By automating the build process, model versioning best practices are deployed to the models ensuring each model is traceable to a specific version of secure, tested code. (Check out where this was highlighted in the Model Versioning: Reduce Friction. Create Stability. Automate blog. Once a model developer checks in their code to version control, the AI model image is built, scanned, and tested making it ready for any hand-off or deployment. This simple, convenient process makes automated builds something developers will seek out, rather than a burdensome business practice.

Empowering teams of data scientists and machine learning engineers through robust practices of CI and containerization will serve to bridge the gap between AI development and deployment at scale.

Visit modzy.com to learn more.

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