So, when I think of MLOps frommodel centric to data centric – I find it hard to distinguish between MLOps itself
And for AI practitioners, MLOps is not new.
In fact, I would argue that if you are a large bank or similar institution, you could not risk deploying a model without MLOps
The second point is, model centric vs data centric is a dichotomy but in reality there are more than two elements.
For example, you would need to consider at least data, models and features instead of just model vs data.
Analysis
The original discussion is framed as:
Would love your feedback on this: AI Systems = Code (model/algorithm) + Data. Mostacademicbenchmarks/competitions hold the Data fixed, and let teams work on the Code. Thinking of organizing something where we hold the Code fixed, and ask teams to work on the Data.
Hoping this will more closely reflect ML application practice, and also spur innovative research on data-centric AI development. What do you think?
I think in the above the operative word is ‘academic’
If so, that brings more clarity
AI is a unique discipline because it brings academic research with practise much more closely than other disciplines.
And the two worlds are quite different.
So, while MLOps is the norm for practitioners, it may not be so obvious to all as different perspectives amalgamate.
Some more comments
Raising the significance of good data for a model is always a good idea
In larger projects, at least three job types work together (data engineers, data scientists and devops engineers). So, again, there is value in raising the awareness of MLOps
Are we trying to say that MLOps should be about ensuring that data is consistent and of high quality throughout the project lifecycle? That could mean a data driven emphasis on MLOps. Raising that awareness is also a good view – as per the comment “Important frontier: MLOps tools to make data-centric AI an efficient and systematic process. “
To conclude
I find the framework of Model-centric to Data-centric AI limiting in the sense of holding model fixed and vary the data or vice versa. But nevertheless, it helps to raise awareness of data itself and could be useful when different perspectives of AI interplay
With the rise of technologies, our day-to-day has completely transformed. As the transit of time, we are witnessing some major changes in the web development space.
Technologies like IoT, AI, AR, VR is making human life simpler and perform monotonous tasks quickly. The advanced web development technologies offer top-notch products to people, which help them do tasks with fewer complications.
Introduction
Many products have been launched in the online world in recent years, which are most popular these days. The product reviews with interactive graphics can help your business’s website to stay ahead in the cutthroat competitive world.
With these advanced features and functionalities, you’ll leverage the perks for your website, which gives your business a huge profit. There are numerous updated changes available with the assistance of technology, the changes can work in the favor of web development requirements.
If you plan for website development or run an eCommerce website, you need to read the following latest advancements. Here are many things available which help you generate huge revenue on investment, look:
Artificial Intelligence and Bots
In the upcoming years, bots will be capable of self-learning and fulfill the users’ requirements. This means that bots will perform the work of executive employees, which eventually helps the companies in saving money. Giant B2C companies are already using this technology to answer their customers- chatbots inside social media platforms are one of the examples. Web development companies are now integrating bots on the website, PWA, and mobile applications.
Bot’s rise will make advent changes in website design, user experience. The important top-notch technology helps custom web development companies with virtual help design. The technology isn’t only help with visual experience, but also helpful in producing sound design. The most significant perk of AI-powered bots is that it has problem-solving potential, understand human behavior.
Accelerated Mobile Pages (AMP)
Accelerated Mobile Pages (AMP) is one of the trendiest web development practices. The motive is to reduce the loading time of web pages and enhance page performance. AMP technology is like PWA. Twitter and Google develop these AMP technologies with the help of the open-source plugin.
AMPs are customized pages that function fast and have a simple yet interactive web design with some basic features, functionalities to offer their services and products. These pages are user-friendly and their content has high readability.
With the emergence of 5G internet connectivity, companies are usually opting for native apps for enhanced user experience, but AMP plugins help brands save time. Because of this, small-scale startups can make their step into the free market world of technology.
Single Page Application (SPA)
Nowadays, with high-speed internet connectivity, you won’t be stuck on the web pages, you can click on multiple pages to download from a server. Single page application is the latest web development trend that helps companies in ignoring unwanted communication and showcases enhanced web page performances and offers prominent protection to the website.
With the help of the JavaScript framework, SPAs gain widespread popularity. The JavaScript framework helps browser events and runs the web applications smoothly. Google services like Gmail, Google Drive, or Google Maps and social media platforms like Facebook are some of the examples. The latest web trends make the websites more constructive, enhanced functionalities.
SPAs are designed to engage the users’ retention for a long time and help the website operate quickly. SPAs offer instant feedback to the companies and faster than the regular sites without server-side code at all (API technology).
Voice Search Optimization
The web development of future trends includes voice searches and it’s not only about Siri, Alexa, and Google Home. In the year 2021, more than half of the smart devices include voice recognition searches. And even more–the development can recognize the voices of people and offers a customized AI experience to the users.
Voice search is the technology of the future that was implemented more than a decade ago. In the year 2021, people don’t want to text or type commands. That’s why companies are looking for methods that help them make digital products integrated with voice command features. According to the reports, 55% of all the households’ devices are going to have voice assistant features.
The trend in web development is to launch voice recognition features in websites and applications. Powered with AI, voice searches help the owners and end-users quickly. First, it consumes a lot of time for users to place an order online on eCommerce websites. Secondly, the technology is multitasking. Third, it fetches the users’ loyalty, which helps them cope up with their routine. The most significant point is it helps companies to understand the users’ behavior.
Motion UI
Innovation web design is one of the latest trend brands in the digital world. As the transit of the years, the startups are focusing on fetching the user’s attention through innovative design. However, appealing designs have a high chance to get noticed by users. Visual design is now considered as the marketing strategy.
Experts are now integrating motion UI into their web apps and websites. Motion user interface design has been the current market demand. Since the year 2018, the motion user interface has been widely accessible across the devices with the help of SaaS libraries technologies.
Motion UI design makes the websites more interactive. The approach involves CSS transitions included with libraries and animated elements. Developers spend less time on building digital products, which eventually saves the price of web development.
Automation Testing
Most web technologies are developed with a motive to make the entire development process affordable and offer a remarkable user experience. Machine learning and the Artificial intelligence method let us develop difficult projects with fewer experts, while the automated testing tools help us check that the products are ready to launch or not.
Test automation has several advantages including beta testing coverage, bug detection, and code transparency. The method helps the development team fetch test cases and reduce the development cost and time. The test automation will offer better products in less money.
We all want to know why automation technology is still relevant in the year 2021? The answer is the digital world is getting more competitive. To survive in this cutthroat competitive world, you need to build a product that performs better than your competitor companies.
JavaScript Frameworks
JavaScript is one of the best programming languages in the world, which is the reason companies are still discussing it. In the year 2021, website trends include many aspects of JavaScript frameworks.
JavaScript framework is considered as the prominent front-end development ecosystem. In the year 2021, JavaScript includes UX, UI, and testing, and code management. The framework offers components one requires building websites.
This web trend has a lot of advantages. Immediate feedback and reviews are provided to the users regarding the page performance, code efficiency, and design testing. Mind that the latest version of the framework is comparatively better than the old version. Integrated with HTML templates, component-based design, data management tools that build the system more robustly.
Serverless Applications and Architecture
Serverless technology solves problems like data loss, system overloading times, and expensive development. Powered by providers like AWS, it developed serverless algorithms as cloud-computing models.
The serverless app models help companies in development cost, robust the application with advanced features and functionalities with a safer internet environment. Cloud computing services offer a chance to build, execute and app features and functionalities without building a product architecture by themselves.
Chatbots, IoT apps are examples of serverless technology. One task that needs to be excluded from the serverless technology is notification delivery, downloading files back. The top service provider of this technology is Google Cloud and Microsoft Azure.
Blockchain Technology
Recently, a report states that there are over 34 million bitcoin users across the globe. The technology involves software and hardware, legal regulation varies countries wise and a place for trading. Cryptocurrencies aren’t just about bitcoins. It’s about integrating secure, protected features into online e-wallet platforms. Blue-chip bank companies are building strong algorithms for protecting account holders’ data.
Cryptocurrencies aren’t the trendy web development technology. The technology was launched in the year 2004. Three years ago, the crypto trading market was built. Currency trading can’t be ignored by authorities. The utilization of blockchain technology has increased in the last few years and bitcoin and Cryptocurrencies are considered major payment systems.
Internet of Things (IoT)
IoT(Internet of Things) is a technology that interconnects the devices and helps the user to perform tasks without keeping their bodies in action. In the year 2021, some major web development trends are the example of IoT development.
According to the report, there will be over 30 billion IoT devices available in 2025. We expect that in the upcoming years there is going to be a huge requirement for IoT solutions. The basic concept of developing IOT devices is to make human life easy. Automation methods like automatic payment, smart home devices, and an online healthcare environment, allows humans to think about important things.
The popular industries which are likely to opt for IoT devices are transport, healthcare, and housekeeping. IoT technologies also help web development companies in UX. We might see companies using IOT technologies in voice interface design.
Mobile-First Development
The number of smartphone users is increasing rapidly. 54% of the internet traffic is consumed with mobile phones and tablets. So we shouldn’t be surprised to know that website design should be according to the mobiles’ screen sizes.
Mobile-first development is an idea in which you think about your web solutions according to the mobile screen and other hardware features. In simple words, you need to develop a web product that makes it easier for users to interact with your business through mobile phones.
It is advised now to deliver content with fewer complications, elements, and reduce page loading times. Include call-to-action, use a simple yet attractive design, and a simple and bright color scheme. This is significant to understand that since 2018, search engine Google has made some drastic changes in the algorithm pattern for mobile phone customized websites.
Responsive Websites (RWD)
The concept was launched 5 years ago when mobile industries were making enormous steps in the market. In the year 2019, most web searches are made by mobile users, which means that companies need to integrate advanced features to make their websites convenient for mobile phones and desktops.
Companies have a few major options: mobile-first development, invest in responsive websites. Responsive websites are developed using HTML codes with CSS templates which let the website render properly according to the devices’ screen size. The motive is to develop mobile-friendly web solutions. This technology is gaining immense popularity.
Push Notifications
Push notifications are the notification which users receive when they are not using the applications. Companies are using push notifications to fetch users’ engagement, improving conversion rate. Giant platforms like Facebook, Google Mail already integrate push notification technology into their web apps.
In the upcoming years, we might witness small and medium-scale companies using these technologies. There are chances that this technology might replace email marketing and some other type of communication that companies do to connect with customers.
GDPR and Cyber Security
With automation operations like online payments, booking flights, paying taxes, and even using smart home devices, there is always a risk of a data breach. The cyber securities industry is likely to hit $300 billion by the end of the year 2024. At least we are expecting that web development companies should focus on developing solutions, which save our data from phishing attacks.
The expansion of data privacy policies such as GDPR (General Data Protection Regulation) is the up-to-date web development market trend. The authorities have the power to put a fine on Internet vendors if they collect the users’ data, and if they sell or use their data without their permission.
Summing up
The above technologies are going to make a tremendous impact on the web development process. You need to build websites that conveniently communicate with the users and fulfill their requirements without complexities.
In this article, I describe the various steps involved in managing a machine learning process from beginning to end. Depending on which company you work for, you may or may not be involved in all the steps. In larger companies, you typically focus on one or two specialized aspects of a project. In small companies, you may be involved in all the steps. Here the focus is on large projects, such as developing a taxonomy, as opposed to ad-hoc or one-time analyses. I also mention all the people involved, besides machine learning professionals.
Steps involved in machine learning projects
In chronological order, here are the main steps. Sometimes it is necessary to recognize errors in the process and move back and start again at an earlier step. This is by no mean a linear process, but more like trial and error experimentation.
1. Defining the problem and the metrics (also called features) that we want to track. Assessing the data available (internal and third party sources) or the databases that need to be created, as well as database architecture for optimum storing and processing. Discuss cloud architectures to choose from, data volume (potential future scaling issues), and data flows. Do we need real-time data? How much can safely be outsourced? Do we need to hire some staff? Discuss costs, ROI, vendors, and timeframe. Decision makers and business analysts are heavily involved, and data scientists and engineers may participate in the discussion.
2. Defining goals and types of analyses to be performed. Can we monetize the data? Are we going to use the data for segmentation, customer profiling and better targeting, to optimize some processes such as pricing or supply chain, for fraud detection, taxonomy creation, to increase sales, for competitive or marketing intelligence, or to improve user experience for instance via a recommendation engine or better search capacities? What are the most relevant goals? Who will be the main users?
3. Collecting the data. Assessing who has access to the data (and which parts of the data, such as summary tables versus life databases), and in what capacity. Here privacy and security issues are also discussed. The IT team, legal team and data engineers are typically involved. Dashboard design is also discussed, with the purpose of designing good dashboards for end-users such as decision makers, product or marketing team, or customers.
4. Exploratory data analysis. Here data scientists are more heavily involved, though this step should be automated as much as possible. You need to detect missing data and how to handle it (using imputation methods), identify outliers and what they mean, summarize and visualize the data, find erroneously coded data and duplicates, find correlations, perform preliminary analyses, find best predicting features and optimum binning techniques (see section 4 in this article). This could lead to the discovery of data flaws, and may force you to revisit and start again from a previous step, to fix any significant issue.
5. The true machine learning / modeling step. At this point, we assume that the data collected is stable enough, and can be used for its original purpose. Predictive models are being tested, neural networks or other algorithms / models are being trained with goodness-of-fit tests and cross-validation. The data is available for various analyses, such as post-mortem, fraud detection, or proof of concept. Algorithms are prototyped, automated and eventually implemented in production mode. Output data is stored in auxiliary tables for further use, such as email alerts or to populate dashboards. External data sources may be added and integrated. As this point, major data issues have been fixed.
6. Creation of end-user platform. Typically, it comes as dashboards featuring visualizations and summary data that can be exported in standardized formats, even spreadsheets. This provides the insights that can be acted upon by decision makers. The platform can be used for A/B testing. It can also come as a system of email alerts sent to decision makers, customers, or anyone who need to be informed.
7. Maintenance. The models need to be adapted to changing data, changing patterns, or changing definitions of core metrics. Some satellite database tables must be updated, for instance every six months. Maybe a new platform able to store more data is needed, and data migration must be planned. Audits are performed to keep the system sound. New metrics may be introduced, as new sources of data are collected. Old data may be archived. Now we should get a good idea of the long-term yield (ROI) of the project, what works well and what needs to be improved.
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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). You can access Vincent’s articles and books, here.
Personal information theft for various crimes has developed a huge concern among customers for their data safety and security. Be it any industry, protecting consumer data should be on the top of the priority list and the healthcare industry is no exception. Hence a law called HIPAA (Health Insurance Portability and Accountability Act) was introduced in 1996 that guided different organizations to protect patient’s personal data. Let us know more about it.
Who Needs to be HIPAA Compliant?
Broadly, it is necessary for everyone who is anyhow associated with ePHI (Electronic Personal Health Information), which includes all organizations in the health sector. Not just hospitals and nursing homes, but agencies and service providers doing business with covered entities also have to be HIPAA compliant.
Any business engaged in supplying goods or providing professional services (attorneys, accountants, and consultants) to covered entities must follow HIPAA rules. Not adhering to the rule can land a business into a serious situation where they would end up paying hefty penalties.
Indeed, being aware of the requirements for compliance is the need of time. So, how to keep data HIPAA compliant?
Ways to Keep Data HIPAA Complaint
Before you make a move, you must be aware of the five critical aspects of a HIPAA compliance program, i.e., Privacy rules, Security rules, Transaction rules, Identifier rules, and Enforcement rules. Thorough knowledge of the compliance will make it easier to address each given solution adequately. Now, let us jump to the solution:
1- Distribute HIPAA policies and procedures to staff.
Every staff member should be aware of the compliance that they have to follow. Create copies of HIPAA policies and procedures in easy-to-understand English language and assign them to all staff members. Make sure all the staff members read the copies and attest to the HIPAA policies and procedures they received.
Also, document all the staffs’ attestation as a record to prove that the organization has distributed the rules. The documentation should also have the annual reviews of your HIPAA policies and procedures.
2- Train employees through a basic HIPAA compliance program.
Simply distributing copies of policies and procedures is not enough. The staff members also have to be trained about it so that they can practically implement the rules flawlessly.
After the training program does not forget to keep its documentation which would be required during the audit. Additionally, appoint a staff member as the HIPAA Compliance, Privacy, or Security Officer who will be responsible to implement policies, procedures, and standards under HIPAA rules.
3- Identify all business associates defined under HIPAA rules.
Your organization might have associates who are receiving, transmitting, maintaining, processing, or accessing ePHI. Identify them all, as they all are defined under HIPAA rules and must have a Business Associate Agreement (Business Associate Contract) in place for each such associate. Even if you are an associate, always ensure you have a copy of the agreement.
Once you have the list of the associates, audit them to make sure they are compliant with HIPAA rules. As proof, create a report of the audit and document it.
4- Develop a management system to handle security incidents/breaches.
Handling security incidents or breaches is the job of the professionals. Have a dedicated management system in place to address such issues. The system must also allow staff members to anonymously report any security incident if needed.
Plus, you should track and manage all investigations of any incidents relating the PHI security. Create a record of all incidents of breaches, be it minor or significant. So that you can demonstrate your investigation to the assigned auditors.
5- Conduct an annual risk assessment.
Self-audit is a good way to start with. But, later on, go for afully independent auditing team for HIPAA assessment, comprising certified engineers and compliance experts. The team ensures you go through an unbiased audit. Investing over an auditing service is sensible since risk assessment is a complex process that involves identifying multiple possible risks to an organization and addressing any vulnerabilities relating to network security.
Additionally, they are knowledgeable enough to educate you about the compliance, are experienced experts, provide support and protection required to secure PHI, and help clear your audits.
6- Conduct regular penetration testing and vulnerability scans.
Frequently scanning vulnerabilities clarifies the criticality involved and the network and data protection. Make sure your auditing team tests the security on a monthly or quarterly basis. Ask them for a complete report of external, internal, and web application testing to add to your record. They should also provide you with strategies or remedies to overcome the shortcomings.
7- Strengthen application security.
Technology is evolving every day and so are the cybercriminals, looking for every possible flaw in the security layers to get into your ePHI system. Secure every element of your web-facing applications, including the design, development, and deployment. Update the applications and security apps regularly.
Assess the application thoroughly for any vulnerabilities and address any design flaws immediately. Never ignore any security gaps that might compromise security and HIPAA compliance. Additionally, get screen servers, privacy screens, and professionally managed technology solutions to build additional security layers. Managing and fixing risks right on time will save both your time and money.
Summary
Adhering to the HIPAA rules is not only legally important but also necessary to ensure that patients can trust you with their personal health information. The rules are designed in a way that ensures that every entity that collects, maintains, or uses confidential patient information keeps it safe.
Nowadays, organizations use a SAS-based MR (Medical Records) solution. But does not exempt them from taking responsibility for maintaining patient data privacy. As a covered entity or provider, it is completely your responsibility to protect the data.
The above-mentioned tips will help you stick to HIPAA compliance, but at the same, it is essential to understand that it is incredibly challenging to do so by yourself. You must consider getting professional help to avoid lax security.
Data science steps in when traditional modeling methods fail.
Outbreak analytics can handle the complex data involved in pandemic assessment.
The last two decades saw more new infectious diseases of global concern than in any similar period of history [1]. Ebola, Influenza A (H1N1), SARS, MERS, Zika virus were all of concern, but it wasn’t until the COVID-19 pandemic hit full force that the world became acutely aware of the inadequacy of the traditional outbreak response system. Despite officials’ best efforts to contain the Covid-19 outbreak, the traditional methods—surveillance, response, and management—failed to address ample warnings, in part because of inadequate procedures for handling diverse data.
The successful investigation and containment of infectious disease outbreaks relies on analyzing complex and diverse data sources. Analysis of this data can only be achieved by a multidisciplinary approach, using several different, complementary approaches and tools including outbreak analytics. Clinical researchers and medical professionals around the globe joined forces to find ways to handle complex data, open-source data, and work collaboratively on improvements to the system [2].
An Overview of Outbreak Analytics
Outbreak analytics was developed to focuses on the technological and methodological aspects of the outbreak data pipeline from data collection to informing outbreak response. It sits at the crossroads of data science and a variety of public health fields including planning, field epidemiology and methodological development. Outbreak analytics is part of an overall prevention and control plan that includes several other core pillars of outbreak response including case management, surveillance and contact tracing, logistics, and testing [3].
The interdisciplinary field uses data science methods from a variety of perspectives to inform outbreak response, including [4]:
Bayesian statistics,
Database design and mobile technology,
Evidence synthesis approaches,
Frequentist statistics,
Geostatistics,
Graph theory,
Interactive data visualization,
Mathematical modelling,
Maximum-likelihood estimation,
Genetic analysis.
It can help to answer questions like [4]:
What are mortality and risk factors?
Could a rapid test help reduce incidence?
Which is the optimal vaccination strategy?
Should international travel be restricted?
Has the delay between symptom onset and hospitalization been reduced?
Despite many advances in outbreak analysis over the last 18 months, like contact tracing, pandemic modeling and risk assessment, adoption of outbreak analytics has been at a snail’s pace. A unified platform for the analysis of disease outbreaks is still lacking [5].
The Future of Outbreak Analytics
The emergence of outbreak analytics highlights the need for freely available, high-quality, and open-source methods for handling infectious disease outbreaks. While not yet fully recognized as a field deserving of recognition and support [6], it’s likely that the development of outbreak analytics will continue to grow. Many agencies including the World Health Organization and UNICEF have already implemented outbreak analytics into their programs. Earlier this year, a major step towards recognition was made when the Assistant to the President for National Security Affairs (APNSA), in coordination with various other coordinators and agencies, were directed by the Whitehouse to develop a plan for establishing an interagency National Center for Epidemic Forecasting and Outbreak Analytics [7]. Going forward, as globalization leads to increasing pandemic risk, expect to hear a lot more about this emerging field of data science.
The use of data science and artificial intelligence for economics and finance is providing benefits for scientists, professionals and policy-makers by improving the available data analysis methodologies for economic forecasting and therefore making our societies better prepared for the challenges of tomorrow.
This book is a good example of how combining expertise from the European Commission, universities in the U.S. and Europe, financial and economic institutions, and multilateral organizations, can bring forward a shared vision on the benefits of data science applied to economics and finance; from the research point of view to the evaluation of policies on the other hand. It showcases how data science is reshaping the business sector. It includes examples of novel big data sources and some successful applications on the use of advanced machine learning, natural language processing, networks analysis, and time series analysis and forecasting, among others, in the economic and financial sectors. At the same time, the book is making an appeal for further adoption of these novel applications in the field of economics and finance so that they can reach their full potential and support policy-makers and the related stakeholders in the transformational recovery of our societies.
The book is entirely published as Gold OA to reach a large audience. Here are the links:
This book follows up another previously published Springer volume titled: “Data Science for Healthcare: Methodologies and Applications”, which was co-edited by Sergio Consoli, Diego Reforgiato Recupero, and Milan Petkovic, that tackles the healthcare domain under different data analysis angles.
Considering the number of recent initiatives that are now pushing towards the use of data analysis within the economic field, we are pursuing with the present book at highlighting successful applications of data science and artificial intelligence into the economic and financial sectors.
We believe the topics dealt by the book to be extremely relevant nowadays within the scientific community, and that the book would be an interesting read for the related audience to let them be acquainted with the latest advancements on these subjects.
Industries are walking the path that leads to digital transformation and automation, and artificial intelligence is the constant companion. Not many know that artificial intelligence remained stagnant for decades but is now undergoing massive growth and development.
Then comes the subset of AI- Machine Learning. Now the question is, what makes AI so powerful? Well! The answer is the presence of the most popular programming languages for machine learning and artificial intelligence.
In this article, we will take you through the best programming languages for AI and ML. However, we will first re-introduce AI and ML to you. Let’s dive in!
What is Artificial Intelligence?
Artificial Intelligence is a branch of science that builds smart machines capable of performing tasks that otherwise require human intervention.
While addressing a crowd at the Japan AI Experience in 2017,DataRobot CEO Jeremy Achin was seen quoting, “AI is a computer system able to perform tasks that ordinarily require human intelligence… Many of these artificial intelligence systems are powered by machine learning, some of them are powered by deep learning and some of them are powered by very boring things like rules.”
What is Machine Learning?
Machine Learning refers to the science of getting computers to learn and act the way humans do and improving their learning over time in an autonomous manner.
Top 5 Programming Languages of AI & ML:
1. Python
Did you know?
More than 8.2 million developers across the globe use Python for coding.
Well! This is why Python is considered to be the best programming language for machine learning and artificial intelligence. Its simple syntax, less coding, and availability of ready-to-use libraries make it even more popular amongst developers.
Python is an open-source AI programming language. Moreover, it can be used to make small scripts, making it again more suitable for AI.
The most imperative fact about Python is that it is easy for students to understand and learn because it uses English keywords. Besides, it has only a few keywords and has a precisely defined syntax.
It also provides interfaces for the majority of commercial databases. With high scalability, this language is easy to learn and is one of the most popular languages for AI today.
Python can also be integrated with C, C++, Java, Cobra, and various other languages. Additionally, it also supports Object-oriented programming (OOP) and dynamic type checking.
2. Java
Did you know?
As of early 2020, Java was the most commonly used programming language among software developers.
Java comes second amongst the most popular programming languages for machine learning and artificial intelligence. It has grown since its emergence in 1995 and has become a highly portable, maintainable, and highly transparent programming language.
The language is easy to implement on different platforms because of its Virtual Machine technology. In simpler words, once it is written and compiled on one platform, the developers do not need to compile it again. Various advantages of Java as an AI language are easy to use, fast debugging, portable, and automatic memory manager.
At present, Java is proving to be highly versatile and can be used in robot systems, sensors, and machine learning suites.
3. Julia
Did you know?
Julia is lightweight and can run even on the tiniest computers.
Julia is the best language for AI and machine learning, especially when a task demands high-performance numerical computing and analysis. It is an open, dynamic compiled language that focuses on performance computing.
The programming language is a result of a combination of qualitative environments’ functionality like R and Python. On top of it, Julia possesses the speed of production programming languages like Java and C++ to solve Big Data and analytics-based problems.
It enables the translation of algorithms from research papers into code without any loss, reducing model risk and improving safety. Moreover, a reliable machine learning consulting company can always guide you on the right path by making the most appropriate use of programming languages like Julia.
4. LISP
Did you know?
LISP is known to be the oldest AI programming language.
LISP (List Processing) was created in 1958 by John MacCarthy. It can be effectively used for machine learning as it is highly flexible and adapts to the solution.
Being the best language for AI and machine learning, it is known for rapid prototyping and the dynamic creation of new objects. Developers rely on LISP in cases where AI projects are heavy on ML because of its rapid prototyping capabilities, a library of collection types, and quick adaptation to problem-solving needs.
You can enable interactive evaluation of expressions and recompilation of functions concurrently while the program is running. LISP is a dynamically typed programming language that has positively impacted the creation of many machine learning programmings languages like Python, Julia, and Java.
Surprisingly, it has the capability to code, compile, and run code in more than 30 programming languages.
5. C++
Did you know?
C++ was first standardized in 1998.
C++ is the most obvious option for AI and deserves to be called the best language for machine learning. Search engines use C++ to leverage less response time. In addition, it is an extension of the C programming language and can be used to build neural networks.
The best advantage of the language is its fast calculations that solve complex computations of AI development. Moreover, it is cost-efficient as compared to other programming languages.
Holding the capability of both a low-level and high-level programming language, C++ comes with a higher level of control and efficiency as compared to any other language.
Conclusion
Things change every day, and so does the best programming language for machine learning and artificial intelligence. Admittedly, you cannot consider one programming language to be the best while ignoring others. Act rationally by looking for the best IT consulting companies in NYC to furnish yourself with all the relevant information and begin your journey to exploit AI’s potential. The above-mentioned programming languages can transform the marketplace digitally and multiple organizations have already begun considering it seriously.
The world’s interest in AI is growing exponentially. At which level is your organization’s interest?
Mobile applications prove helpful to users and are work as great promotion tools too! Along with that, they increase engagement, enable people to learn in different ways and also generate a new and more widened user base! This is applicable to all kinds of applications! Something to learn from…
We are witnessing an era in which technology has seeped into education and renewed its whole teaching and process – especially eLearning. It is an educational tool that has not only increased the accessibility and convenience of education but has also changed the learning behaviours and learners’ desire to learn anything! Learning is a constant process, it never ends. So, for today’s blog I thought to myself… why not focus on educational tech trends?! Technological advancements have not left a single domain untouched. They are helping everybody to educate themselves, be socially relevant and simply have fun – all of this with the help of smart devices!!
The awe-inspiring and interesting trends!!
Artificial Intelligence (AI) AI is the not technically a new thing, but has got people raving all over! It has been predicted that through the year 2021, it can become one of the primary trends and can grow by more than 45%!! Though already an awesome technology, let us try and understand why it has been trending in the EdTech market… Artificial Intelligence has the capability to automate basic activities in education – grading, for instance. This imparts speed to the teaching process and checking assignments, etc. Thus, this technology benefits learners and educators both from AI. Helping students and approaching tutors both become possible! Therefore, it is not too far fetched of an idea that AI is a powerful teaching assistant.
Immersive learning There exists no domain that has been left untouched by tech advancements. AR and VR are another pair of amazing technologies that offer immersive experiences and they find a huge fan-base in the education sector too. Digital content, online assessments, smart classes are slowly transforming the education sector, for the better.
Incorporating new and wonderful technologies like Augmented and Virtual Reality can work wonders – for both, students and teachers. Teachers can teach in a more interactive and interesting manner. The students can learn more efficiently and efficaciously.
Mind you, AR and VR can be implemented at all levels, from elementary to higher and then research studies. They ensure faster learning, provide better outcomes to learners, fuel vocational training, etc. Thus, it wouldn’t be wrong to claim that these techs can have a wonderfully meaningful impact on students at all levels.
E-Learning It is a system, that is based on formalised teaching with help of electronic resources. The use of the internet, computer, digital devices, etc. is a major component of e-learning. It is also known as m-learning (mobile-learning). Distance learners also use such technologies and this (also) falls under the ambit of e-learning. It involves educational apps, podcasts, educational blogs, online teaching platforms, etc. They facilitate and ensure that users learn better. They also offer support in an engaging, interesting and simple way! With eLearning, educational content is delivered to learners through computers, laptops, tablets, or smartphones. This is not only saving time but opening many doors for interactive learning. Rather than being in a passive experience, learners can choose what they need to learn quickly and easily, wherever they are.
To sum things up….
Technologies that help in educating, are a boon! They benefit all three stakeholders – business owners, students and educators. The latter two, get a platform to learn and teach, respectively.
They allow students in particular and learners in general, to learn online and reap the benefits of this process. What is better than an option that has the potential to be a top-notch business idea, along with being highly helpful, right?!
Do you have any thought or thoughts on similar lines? Then, don’t sit on it any longer and move ahead with educational technology trends in 2021-2022!
Connect with a competent company that provides application development services and the finest web solutions, while staying abreast with the latest technological advancements.
Online education apps and associated trends are the real deal. Entrepreneurs have the opportunity to develop different kinds of engaging applications for diverse educational situations, size of the learners no bar! Witness your dream become a reality through an intelligent virtual network that will deliver high performance!
The Healthcare sector is one sector that is always in demand. Lately, the rate and necessity of patient responsibility and innovative medicines have developed. With the growth in such requirements, the latest technologies have been adopted in the industry. One such significant development that had to take place is the application of Big Data and Analytics in the Healthcare sector.
It has been observed that big data are predicted to expand faster in healthcare than in areas like financial services, manufacturing, and so on.
Let Us Understand, What Is Big Data In Healthcare?
Big data in healthcare is a word utilized to explain massive volumes of data generated by the determination of digital technologies that manage patients’ experiences and support in training hospital administration, otherwise too long and heavy for old technologies.
The purpose of big data analytics in healthcare has a lot of definite and also life-saving results. In reality, big-style data relates to the enormous quantities of data generated by the digitization of everything that gets mixed and analyzed by particular technologies. Employed in healthcare, it will use precise health data of a group or an appropriate person and possibly help to stop diseases, cure illness, cut down prices, etc.
Now that we live longer, practice patterns have improved, and many of these changes are particularly inspired by data. Experts need to understand as much as they can about a victim and as early as feasible, to pull up notice signs of severe illness as they appear – managing any condition at an early step is far more manageable and less valuable. By appropriating key performance pointers in healthcare and healthcare data analytics, blocking is better than remedy, and training to form a whole picture of a patient will let support produce a tailored set. The industry endeavors to tackle the serious problems a patient’s data has: everywhere are collected pieces and bites of it and archived in clinics, surgeries, hospitals, and others, with the failure to communicate accurately.
Certainly, for years collecting huge numbers of data for medicinal use has been expensive and time-consuming. With today’s always-improving technologies, it becomes more apparent not only to receive such data but also to build overall healthcare statements and transform them into proper crucial insights that can then be utilized to implement better care. It is the purpose of healthcare data analytics: applying data-driven decisions to the divine and solving an issue before it is too late, but also assess methods and treatments faster, keep a better record of the list, include patients more in their health, and approve them with the instruments to do so.
Variable Big Data Applications In Healthcare
The potential of big data is reshaping the aspect and dimensions of various areas, particularly healthcare strangely and unpredictably. Let’s see the influential reasons for Big Data in the Healthcare sector.
1) Limiting Human Errors
A lot multiple times- it has been remarked that the experts manage to either prescribe the incorrect medicine or receive a different medication by error. Such flaws, in usual, can be defeated since Big Data can be leveraged to examine user data and the prescribed medicine. It can support the data and flag possible community prescriptions to defeat blunders and save lives. Such software can be a vast machine for physicians who provide too many patients in a single day.
2) Tracking of Health
Big Data and Analytics onward with the Internet of Things (IoT) are transforming the process one can trace different user statistics and vitals. Aside from the essential wearables that can identify the patient’s dream, exercise, distance hiked, heart rate, and many more. Different medical reforms can control the patient’s blood pressure, glucose monitors, pulse oximeters, and more. The constant monitoring of the body vitals along with the sensor data store will enable healthcare organizations to put people out of the dispensary since they can recognize latent health issues and give care before the circumstances go more serious.
3) Improving Patient Commitment
Several customers and therefore, possible patients already have an advantage in smart devices that register every move they take, sleeping schedule, heart rates, and much more lastingly. All this necessary data can be linked with other trackable information to recognize possible health risks hiding. Prolonged sleeplessness and a raised heart rate can indicate a prospect for future heart disease for example. Patients are immediately included in the control of their wellness, and influences from health insurance can force them to begin a healthful lifestyle.
Another method to do so appears with different wearables under community, following specific health bearings, and delivering them to the spot where doctors can observe them. Patients experiencing blood tension or asthma could profit from it, and become a bit more self-sufficient and decrease avoidable appointments to the doctor.
4) Decreasing Value
Big Data can be an exceptional way to lessen expenses for hospitals that either above or under-book staff branches. Ominous analysis can improve this problem by foretelling the acceptance rates and assisting with staff allocation. It will decrease the rate of investment acquired by hospitals and in particular, help practices their investment to the max. The insurance business can conserve money by supporting wearables and health trackers to assure that victims do not waste time in the hospital. It can save wait events for patients because the hospital will have sufficient staff and beds available as per the investigation all the time. Forbidding analytics also helps lower costs by decreasing the rate of clinic readmissions.
Final Words
In the upcoming future, the healthcare sector will witness a lot more Big Data applications that will transform the healthcare industry slowly one at a time. Not only will Big Data help streamline the performance of healthcare services, but it will also enable them to improve their aggressive support through forwarding business solutions.
In 2019,Economist Intelligenceconducted a survey of more than 500 companies from eight countries. The study aimed to figure out how far organizations have progressed in automating their activities. Among other economic sectors, the report included some facts aboutbusiness process automation in Retail.
More than half of respondents (51%) from different industries admitted that they actively use robotization in their work. The scale of the introduction of such technologies varies depending on the region and sector. For example, companies in the USA, France, and Germany take the lead in intensive automation. What IT solutions are the most popular in Retail? What is hindering their implementation? How soon will robots replace salespeople? Read about this in our article.
Tools forRetail automation
Automation implies the use of software that independently performs tasks, simulating human work. These tasks are usually repetitive structured activities. At the moment, the most widely used technologies are such software-based solutions as IoT and RPA, which employ Artificial Intelligence. Physical robots play a significant role as well, for example, in logistics and warehouse management.
Retailers actively use the above technologies for collecting and processing data, as well as for performing predictable physical operations. According tothe McKinsey Global Institute, the total automation potential of these and other activities in that industry amounts to 53%.
What processes to automate
Today, retailers are mostly, but not only, automating repetitive documentation tasks. Organizations apply IT solutions in the following areas:
Invoicing and accounting
Using RPA tools enables software to generate reports, reconciliation statements, and cover letters, as well as make payments. The software processes the applications, structures IFRS data, and analyzes it. Moreover, the system keeps records of all transactions, which helps companies successfully pass financial audits.
Customer service
As a rule, activities that involve a high level of human interaction require a combination of standard RPA and AI-driven technology. Such systems don’t just analyze information. They must be flexible to offer the necessary solutions.
One of the tools used in customer support iselectronic consultants. They utilize several techniques simultaneously, including natural language processing algorithms, neural networks, and Deep Learning. Such systems are capable of self-learning, and their effectiveness only increases over time.
Human resource management
Software successfully replaces people when performing HR tasks. In particular, such systems analyze the CVs of candidates on the Internet and select suitable ones. Robots help to onboard new employees and conduct their initial training. Such programs also draw up the letters of employment and reports, keep records of sick leave and vacations.
RPA tools help companies manage purchase orders. Systems automatically send notifications to suppliers, register invoices, take inventory, control the delivery terms, and more. The ML-driven programsplan routesusing real-time data about the traffic and weather conditions. This helps drivers to promptly take action in order to deliver cargo as quickly as possible.
Marketing and sales
Special platforms process customer’s orders, send them notifications, and respond to complaints. Predictive Analytics tools analyze customer behavior on a company’s website and predict demand. Then, this software personalizes offers and adjusts prices, which helps retailers to increase the volume of sales.
Document flow
RPA solutions help retailers to manage the vast amount of various documents. ML-based systems control expiring transactions, create and send reports, register contracts, handle emails, etc.
Software segments and analyzes information based on multiple factors. For example, it considers both historical and real-time data, the information from the currency markets, tax bases, state authorities, etc. Scott Likens, the leader of new services and emerging technologies at PwC, stated that their technologies dealt with the burden of documentation so that the staff could focus on making expert judgments.
Benefits ofbusiness process automation
For many retailers, automation marks the beginning of a digital transformation. According to Economist Intelligence, 73% of organizations are highly satisfied with the outcomes of theirbusiness process robotization. Among the most frequently named benefits of such solutions are an increase in productivity and reduction of errors. The other advantages are improved consistency of operations, increased revenue, and enhanced customer experience.
Difficulties in the adoption of new technologies
If business process automation in Retail is so beneficial, what dissuades organizations from introducing it? Among the most frequent reasons companies name are data privacy concerns, difficulties with technology deployment, and human factors. The latter include the reluctance of employees to accept changes and an insufficient level of skills.
The organizations can partly remove the above obstacles by educating and training their staff members on the RPA solutions. More than half of respondents of the Economist Intelligence survey stated that automation requires the development of new skills in their employees. These abilities include problem-solving, openness to changes, and a teamwork mindset.
The future of automation
Despite the competitive edge that robotics gives retail companies, a lot about the future of this technology is still unknown. How far will automation progress in changing the supply chains, stock management, order handling, and interaction with customers? Will robotization limit or, on the contrary, enhance employees’ creativity and liberty of action? How can companies ensure data protection in the context of rapidly developing technology?
Many retailers are also asking themselves whether cutting-edge solutions will replace the human workforce entirely and how soon it will happen. McKinsey Global Institute concluded that, although machines will play a major role in all economic sectors in the near future, they will successfully perform only one-third of all the tasks. That means the employees will soon need to learn to work alongside rapidly evolving robots.
