In the last year or so, I’ve been watching the emergence of a new paradigm in application development, one that is shifting the way that we not only process data but is also changing the view of what an application is.
This change necessitates a critical idea: the notion that you can represent any information as a graph of relationships. This idea goes a long way back – Database pioneer Ted Codd, alluded to graph-based data systems in the 1970s, though he felt (likely for legitimate reasons) that the technology of the time was insufficient to be able to do much with the notion.
In the early 2000s, Tim Berners-Lee explored this notion more fully with RDF and the Semantic Web, which made use of an assertion-based language as a way of building inferential knowledge graphs. This technology waxed and waned and waxed again throughout the next twenty years, but even as such systems have become part of enterprise data implementations, the relatively static nature of these relationships hampered adoption.
In the mid-2010s, Facebook released a new language called GraphQL, making it open source. GraphQL was intended to primarily to deal with the various social graphs that were at the heart of the social media giant’s products, but it also provided a generalized way of both discovering and retrieving data through a set of abstract interfaces.
Discovery has long been a problem with Service APIs, primarily because it put the onus of how data would be structured on the provider, often with complex parameterizations. and poorly defined output sets. What’s more, any query often necessitated multiple steps to retrieve hierarchical data, reducing performance and necessitating deeply nested asynchronous calls (which ultimately necessitated the development of a whole promise infrastructure for web applications).
GraphQL, on the other hand, lets a client process retrieve the model of how information is stored in the graph and uses that to help construct a query that can then short-circuit the need for multiple calls to the server. Moreover, while a GraphQL server can take advantage of an abstract query language to write simplified queries, resolvers can also be written that can calculate assertions dynamically as well (a major limitation of RDF).
For instance, with GraphQL it is possible to pass a parameter indicating a time zone to a property and the system would then retrieve the current time in that time zone, pulled not from a database but from a function on a clock, even with everything else coming from a database. This ability to create dynamic and contextual properties (at both the atomic and structural levels) may also provide a mechanism to interact consistently with machine learning models as if they too were databases, and may, in turn, be used to simplify updating reinforcement learning-based systems in a consistent manner without the need to write complicated scripts in Python or any other language.
As GraphQL becomes more pervasive, application development will become simpler – connect to an endpoint, build a query, bind it to a web component (in React, Angular, or the emerging Web Components framework), and act upon the results. Because the GraphQL endpoint is an abstraction layer, actions get reduced to traditional CRUD operations, with the difference being that a GET operation involves passing a query construct rather than a parameterized URL, while a POST operation involves passing a mutational construct. Rather than trying to support hundreds of microservice APIs, organizations can let users connect to a single GraphQL endpoint, providing both access to data and protection against exposing potentially dangerous data, the holy grail of application developers and database managers alike.
Developing and implementing software in today’s fast-paced business environment requires the best software development techniques and solutions to ensure your customers get the best service and experience while using your software.
According to asurvey,51% of DevOps users today apply DevOps to new and existing applications. By 2026, its market will undergo a dynamic transition as advancements in automated software development and zero-touch automation technologies drive the DevOps tools’ demand.
The use of DevOps has been proven to help increase the efficiency of the software development process, leading to faster release cycles and ultimately better customer satisfaction with the products you are delivering.
If you’re considering adopting DevOps practices into your organization, then take some time now to learn more about what exactly DevOps is and how it can benefit your business.
What is DevOps?
Adopting a DevOps culture within your organization will help you to accelerate software development. A recent Puppet survey found thatcompanies with a mature DevOps practice have grown over four times faster than those without it. It is mainly due to their ability to experiment and implement changes quickly and easily by building more stability into each release and deploying new updates and features much more rapidly than before. This increased deployment frequency enables organizations to capitalize on market trends or consumer demand more quickly, ensuring they remain competitive in an increasingly fast-paced marketplace. Now, let’s dive into the reasons why DevOps is essential for software development. The Top Benefits of Using DevOps in Software Development:
1) Improved Testing Capabilities
In addition to testing for functional bugs, there’s also a variety of non-functional testing. For example, you can test your system to ensure it’s fast and reliable at a high volume of users—no matter how many people are on your website. Using automated testing will help you scale your systems as needed.
Testing is a vital part of DevOps because its success depends so much on speed and reliability. Your platform needs to handle peak traffic with ease to be genuinely operational, which means frequent regression testing.
2) Increased Quality Products
If you’re struggling to turn software products around promptly, it may be time to adopt a new approach. UsingDevOps software solutionscan help youproduce high-quality products quickly, cutting down on waste and costly mistakes. Even if you have your product development dialed in, these tools might still be worth considering to scale up your business operations.
Deploying even a simple change could take days or weeks under traditional development models. If someone screwed up along the way, everything had to start from scratch. With DevOps practices, once you build software from source code, you can push it out immediately without approval from any of your stakeholders. So long as it meets all relevant criteria (e.g., load tests), anyone on your team can instantly deploy any new feature they want.
3) Better Deployment Processes
When you cannot quickly push out new features and bug fixes, you might find that users aren’t even taking notice of your updates. Adding a DevOps element to your software development process canaccelerate deployment and get new features to your customers sooner. It will get them more engaged with your platform, which is something we all want.
After all, there are plenty of alternatives for them if they don’t like what you have to offer. If they start getting bored or disappointed in what you have on offer, it won’t be long before they go elsewhere—and it could be hard to win them back once they leave for greener pastures.
4) Faster Time to Market
Studies have shown a strong correlation between using DevOps software solutions and getting to market faster. DevOps aims to promote cooperation among developers, sysadmins, testers, and other IT professionals, resulting in an accelerated time-to-market for new products.
Nowadays, every business wants to get to market faster. Using DevOps software solutions is one way to get there. Instead of having each group work independently (as they would with traditional development processes), working together means information gets passed back and forth quickly so things can be changed as soon as they’re discovered. For example, if testing reveals bugs, it’s easier to fix them before deploying than after.
5) Better Security
Security is one of those things that can be hard to measure. It’s also one of those things people tend to only talk about when something wrong happens. Despite all that, it’s essential—especially as we move toward a cloud-based future. Given all that, using DevOps principles and tools willimprove your organization’s security and harden your systems and applications against vulnerabilities and weaknesses. That goes for both your custom solutions and any off-the-shelf software you use. Once you start looking at things from a DevOps standpoint, improving security will become easier until someone launches another round of attacks.
6) Streamlined Operations Team Management
One of my favorite things about DevOps is that it forces teams to get creative about optimizing their customer relationships. In a traditional software development cycle, there’s typically a handoff from marketing and sales to development. It works well for some projects—but can cause problems on more complex initiatives. When DevOps is included from day one, they develop a symbiotic relationship where team members depend on one another throughout every phase of product development. Not only does including your whole team ensure you have access to all relevant perspectives upfront, but it also saves time. It allows your engineers to continue collaborating through delivery instead of becoming siloed when one passes off work to another group.
7) Optimized Customer Relationships
The conventional way of interacting with customers is changing. Businesses are more and more moving towards adoptingcustomer relationship management (CRM)solutions for better managing and monitoring relations with customers. As a result, they can cater to their clients on a one-to-one basis and provide them with an experience that is personalized and fast. This makes it all that much easier to keep your existing clients happy and find new ones. Companies who use CRM will also make better business decisions thanks to having detailed information about their products or services. There are opportunities for growth or areas where things need improvement. Such systems provide businesses with invaluable data and help save time while making data easy to retrieve at any given point in time.
8) No Fear of Change
Doing software development as a service will help you save money and time by ensuring your project runs smoothly. Since you can easily outsource, you’ll never be short of developers when you need them. And, since freelancers work with many clients at once, they tend to handle their tasks more efficiently than those who do it as a full-time job. They also have no fear of change—meaning that if your company’s new direction requires a different approach from what was initially planned, they won’t complain or put up roadblocks. 9) Improved Communication Between Teams & Stakeholders
The most crucial benefit of DevOps software solutions is that itsaves money. It is an efficient way to gain project delivery speed while saving time, money, and effort. It allows businesses to be agile so that they can meet their clients’ demands quickly. It also means developers are more likely to produce better-quality code more quickly, which will save you loads of time later on down the line when looking for someone to maintain your product or service. A company that knows how to use DevOps can outdo its competitors easily due to lower costs. Another great advantage of hiring talented staff like skilled professionals instead of generalists is that everyone becomes an expert at something specific; not many companies can boast of having staff experts in marketing, development, management, etc. If they do have staff experts at one thing, then chances are there isn’t anyone who knows how to handle things outside their field of expertise.
10) Costs Less Money & Saves Time
Before a company implements a DevOps strategy, it has first to know its current software development process. Then it can more easily figure out where time and money are being lost. By switching to a more efficient method of developing software, companies save money and time from those two primary resources and resources on top of them, such as employee morale. Ultimately saving all of these different resources means extra money back into everyone’s pockets; if employees’ hands aren’t tied up with administrative tasks. Everyone who works for or with that business will save both time and money because it will waste less through inefficient software development. They’ll also be able to put their minds toward other innovative ideas which could earn even more revenue for your business.
11) Improved Focus on What Matters Most
An effective DevOps pipeline can increase productivity and allow for easier collaboration between teams—which means developers can focus on what matters most. With a seamless process from code to production,developers can iterate more quickly and deliver better products in less time. A strong focus on automation leads to greater efficiency and consistency of product releases while reducing human error.
Wrapping it Up
If you want to accelerate your software development process, DevOps could be a smart way to do so. With these top reasons and much more below, we hope you can now see how using DevOps can help to propel your company into a much-needed position of dominance. Now is not too late; there is still time for you to get ahead before your competitors—start today!
The use of the internet has penetrated the areas such as availing information, purchasing products, acquiring services, and any aspect a consumer can think of from a business. With the internet taking over almost every business sector today, making your business stand out of the box has become essentially critical for your company’s growth.
Distinguishing your business from others allows you to let your customers identify your brand, build trust, explore your services, and finally engage with your offerings. Hence, having a solid online presence is essential for the business.
It is equally crucial for your business to make the brand presence appealing and engaging. This is where custom web applications development comes in handy for companies.
Developing a custom web application has become much more convenient than it was a decade ago. Thanks to robust web application development platforms like WordPress and Liferay that can build the website without coding.
However, being a business leader, you need to have a proper strategy before developing your website or even mobile application for your business.
To make an impeccable online brand presence, you can choose custom web application development, as it offers out-of-the-box custom solutions to cater to the unique demands of your customers. It can be a perfect solution that can be developed while considering your business’s prerequisites, services, and functionality. In other words, a custom web application development service can entertain all the demands of your customers.
Since we now know the importance of custom web applications development, let’s have a look at eight reasons why every business must have their custom web application:
Eight Reasons for Custom Web Application Development
Stand Out of the Box
The web and application development companies’ custom web application development services offer robust and unique capabilities to your web application. This allows you to make your brand stand out of the box in the market. Moreover, the web application also helps you connect with your consumers personally, making engagement easy and smooth.
Security At Its Prime
Having an online presence brings challenges of security to the table. The risk of losing confidential information via malicious attacks and spyware is something that every business must undertake while planning their web application.
However, the custom web application development service providers keep these challenges in mind and use effective firewalls to keep the data safe. This, as a result, ensures the security of your application and business.
Flexible and Scalable Applications
With the growth of your business, the application needs upgrades. However, predesigned websites and applications are neither flexible nor scalable, limiting their lifespan. The custom web applications are designed with scalability and flexibility to ensure they can adjust to future demands and requirements.
This attribute of the custom web application helps you save tonnes of money and resources. Moreover, with the advent of cloud-native apps, the scalability and flexibility of your business application can further be enhanced adding additional value to your business.
Complete Functionality Control
While designing a web or mobile application, optimization should also be kept in mind along with marketing and branding. Optimization allows the custom web application to operate smoothly and can help you cope with prevailing drawbacks such as unexpected breakdowns and delay in output delivery.
Seamless Journey of the Customer
With the availability of functionalities and several design options, the search efforts for the required products should also be kept to a minimum. It is where custom web application development helps you to make your customer’s journey easy. This improves the customer experience and attracts them to visit your application repeatedly.
Better Business Function Automation
Having a tailor-made or custom web application improves the customer experience. It also helps businesses to optimize internal and external functions. A customized web application will help you with lead generation and attracting prospects, but it will also trim down the efforts of data organization. Moreover, an automated delivery system can share this data with the sales team to convert prospects into customers.
Creative and Attractive Designs
Custom web application development allows you to have a creative and attractive design for your application. This will enable you to attract more customers to the application enhancing the brand value and business growth.
Custom Back-End for Seamless Control
Backend plays a vital role in the smooth operations of your business. Therefore it is equally essential for your business to have a robust backend. Moreover, it should be maintained by someone who possesses knowledge of the details of the application.
The custom web application development service providers allow you to have an expert who monitors and manages your web application’s backend, making it easier for your business to focus on the operations.
To Sum Up
The migration of businesses from brick and mortar to the digital marketplace has allowed the internet to penetrate almost every industry vertical. To cope up with today’s competitive environment, it is critical to have a strong online presence. To achieve this presence, custom web application development is one of the best solutions for your business.
As your team invests significant time and resources developing models, it is imperative that processes are put into place to protect and maximize the return on that investment. To that end, in this installment of the ModelOps Blog Series we’ll discuss leveraging functionality provided by continuous integration/continuous deployment (CI/CD) frameworks such as Jenkins, CircleCI, and GitHub Actions to automate the push of model container images to production container registries. As your team develops and containerizes models, it’s important that they don’t just live on your R&D servers or model developers’ laptops where events like hardware failures or accidental reformats could wipe away capabilities in the blink of an eye. In addition, using a CI/CD pipeline to deploy your models to container registries allows you to do the following in an automated fashion every time you want to release a new version of a model:
Test the model’s functionality and scan for security issues
Store and control access to the model image in a persistent, secure, organized, and scalable fashion
Trace the model image back to its original source code
If configured correctly, this type of automation minimizes the amount of labor required and mitigates the risk of human error through the model deployment process. The starting point for the image push process is a model container image successfully built by a CI/CD server. Make sure you are up to speed on what it takes to produce a model by responsibly sourcing data, following best practices for model training and versioning, and automating model container builds using CI/CD frameworks by checking out the previous posts in this series.
Leveraging container registries
Containerization is important to ensuring models function properly once they are deployed into production. Containerizing models ensures that they will execute in the same way regardless of infrastructure.
A container is a running software application comprised of the minimum requirements necessary to run the application. This includes an operating system, application source code, system dependencies, programming language libraries, and runtime.
A container repository is a collection of container images with the same name, but with different tags.
A container registry is a collection of container repositories.
When working with containerized model images, the container registry might be a collection of numerous container repositories, with each repository corresponding to a particular model. Each of these repositories might contain multiple images corresponding with multiple versions of the model tagged accordingly.
There are numerous options as far as container registries go, including Amazon Web Services (AWS) Elastic Container Registry (ECR), Microsoft Azure Container Registry, and Google Container Registry. Automating the deployment of model container images to these container registries using CI/CD yields a number of benefits. Container registries allow you to easily store, secure, and manage model images. By automating deployment of container images, you can run unit tests to ensure correct model functionality or detect issues early in the deployment process; this includes scanning model images for potential security vulnerabilities. Additionally, if model images are deployed in an automated fashion using CI/CD, tagged model images within repositories within container registries can be traced back to their original source code.
Pushing models to registries using CI/CD
In the previous blog post in this series, we discussed using CI/CD frameworks such as Jenkins, CircleCI, and GitHub actions to automate the building, scanning, and testing of model container images. These CI/CD frameworks also offer support for automating the tagging and pushing of model container images to container registries. For some CI/CD frameworks and container registries, there is built-in compatibility, but for others, additional plugins/configurations are required to successfully automate the push process. Although the process differs in certain ways, container image pushes can be automated using most combinations of popular CI/CD frameworks and container registries.
The Modzy data science team implements a similar process for the models we develop, relying on Github for version control throughout the model development and containerization processes. Every time code is merged to a model repository’s master branch, CircleCI builds, scans, tests, tags, and pushes a new container image to an AWS ECR registry. In this way, bugs or vulnerabilities can be detected prior to the push of the image to the registry, and each image in each repository within the registry can be traced back to its source code using its tag.
What’s next
Now that we have scanned and tested model images built and pushed to a container registry, stay tuned for our next blog post which will discuss the process of deploying models into production.
Despite recent and evolving technological advances, the vast amounts of data that exists in a typical enterprise is not always available to all stakeholders when they need it. In modern enterprises, there are broad sets of users, with varying levels of skill sets, who strive to make data-driven decisions daily but struggle to gain access to the data needed in a timely manner.
True democratization of data for users is more than providing data at their fingertips through a set of applications. It also involves better collaboration among peers and stakeholders for data sharing and data recommendation, metadata activation for better data search and discovery, and providing the right kind of data access to the right set of individuals. Deploying an enterprise-wide data infrastructure with legacy technologies such as ETL, is costly, slow to deploy, resource intensive, and lacks the ability to provide data access in real-time. Worse, constant replication of data puts companies at risk of very costly compliance issues related to sensitive and private data such as personally identifiable information (PII).
As enterprise data becomes more distributed across cloud and on-premises global locations, achieving seamless real-time data access for business users is becoming a nightmare. Modern integration styles like logical data fabric architecture are provisioning data virtualization to help organizations realize the promise of seamless access to data, enabling democratization of the data landscape. When organizations adopt a logical data fabric architecture, they create an environment in which data access and data sharing is faster and easier to achieve, as business users can access data with minimal IT involvement. If properly constructed, logical data fabrics also provide the necessary security and data governance in a centralized fashion.
Critical capabilities and characteristics of a logical data fabric include:
1. Augmentation of information and better collaboration using active metadata – Data marketplaces are important for users to find what they need in a self-service manner. Because a logical data fabric is built on a foundation of data virtualization, access to all kinds of metadata and activation of metadata-based machine learning is easier to build and deploy compared to a physical data fabric. In a single platform logical data fabric, the data catalog is tightly integrated with the underlying data delivery layer which helps a broad set of users achieve fast data discovery and exploration.
Business stewards can create a catalog of business views based on metadata, classify them according to business categories, and assign them tags for easy access. With enhanced collaboration features, a logical data fabric can also help users to endorse datasets or register comments or warnings about them. This helps all users to contextualize dataset usage and better understand how their peers experience them.
2. Seamless data integration in a hybrid or multi-cloud environment – These days organizations have data spread across multiple clouds and on-premises data centers. Unlike physical data fabrics that are unable to synchronize two or more systems in real time, logical data fabric provides business users and analysts with an enterprise-wide view of data without needing to replicate it.
Logical data fabrics access the data from multiple systems, that are spread across multiple clouds and on-premises locations, and integrate the data in real-time in a way that is transparent to the user. Also, in cases where a logical data fabric spans various clouds, on-premise data centers and geographic locations, it is much easier to achieve semantic consistency so that individuals, at any location, can use their preferred BI tool to query data.
3.Broader and better support for advanced analytics and data science use cases – Data scientists and advanced analytics teams often view data lakes as their playground. The latest trend around data lakehouse is to make sure IT teams can support their BI analysts or line of business users as well as data scientists with a single data repository deployment. But there are some inherent limitations to lake houses. Most notably, it requires a lot of data replication, involves exorbitant egress charges to pull data out of lakehouses, and it is impractical to assume one physical data lakehouse can hold the entire enterprise-wide data and the list goes on.
Because a logical data fabric enables seamless access to a wide variety of data sources and seamless connectivity to consuming applications, data scientists can work with a variety of models and tools, allowing each to work with the ones they are most familiar with. A logical data fabric enables data scientists to work with quick iterations of data models and fine tune them to better support their efforts. It also allows them to focus less on the data collection, preparation, and transformation because this, too, can be handled by the logical data fabric itself.
In Closing
While these are some of the most important considerations for deploying a logical data fabric, there are other compelling reasons. For example, physical data fabrics cannot handle real-time integration of streaming data with data-at-rest, for data consumers. As it relates to data security, governance, and compliance, physical data fabric can make enterprise data prone to non-compliance with respect to rules such as GDPR or UK Data Protection Act, for instance. Data security rules cannot be centralized in case of a physical data fabric, forcing IT teams to rewrite data security rules at each application and data source level.
With all these considerations in mind, many Fortune 500 and Fortune 1000 companies are deploying logical data fabric with data virtualization to make data available and self-serviceable for all their data consumers and data stakeholders. Only with a logical data fabric can help any organization truly democratize their data and empower all their globally distributed data consumers.
In 2019, Venturebeat reported that almost 87% of data science projects do not get into production. Redapt, an end-to-end technology solution provider, also reported a similar number of 90% ML models not making it to production.
However, there has been an improvement. In 2020, enterprises realized the need for AI in their business. Due to COVID-19, most companies have scaled up their AI adoption and increased their AI investment.
According to the 2020 State Of The ML Report by Algorithmia, AI model development has become much more efficient. It reported that almost 50% of the enterprises deployed an ML model between 8 to 90 days.
This statistic shows the improvement in enterprise AI adoption.Yet, to completely harness the power of AI in your business, you need to build and deploy multiple models.
In this article, we will be discussing the steps in AI model development. We will also shed light on AI model development challenges and discuss how you can accelerate your enterprise AI adoption.
AI model development involves multiple stages interconnected to each other. The block diagram below will help you understand every step.
We will now break down each block in detail.
Step 1: Identification Of The Business Problem
Andrew Ng, the founder of deeplearning.ai always prefers seeing AI applications as a business problem. Instead of asking how to improve your artificial intelligence, he suggests asking how to improve your business.
So, in the first step of your model development, define the business problem you are looking to solve. At this stage, you need to ask the following questions.
What results are you expecting from the process?
What processes are in use to solve this problem?
How do you see AI improving the current process?
What are the KPIs that will help you track progress?
What resources will be required?
How do you break down the problem into iterative sprints?
Once you have answers to the above questions, you can then identify how you can solve the problem using AI. Generally, your business problem might fall in one of the below categories.
Classification: As the name suggests, classification helps you to categorize something into type A or type B. You can use this to classify more than two types as well(called multi-class classification).
Regression: Regression helps you to predict a definite number for a defined parameter. For example, predicting the number of COVID-19 cases in a particular period in the future, predicting the demand for your product during the holiday season, etc.
Recommendation: Recommendation analyzes past data and identifies patterns. It can recommend your next purchase on a retail site, a video based on the topics you like, etc.
These are some of the basic questions you need to answer. You can add more questions here depending on your business objective. But the focus should be on business objectives and how AI can help achieve them.
Step 2: Identifying And Collecting Data
Identification of data is one of the most important steps in AI model development. Since machine learning models are only as accurate as the data fed to them, it becomes crucial to identify the right data to ensure model accuracy and relevance.
At this stage, you will have to ask questions like:
What data is required to solve the business problem – customer data, inventory data, etc.
What quantity of the data is required?
Do you have enough data to build the model?
Do you need additional data to augment current data?
How is the data collected and where is it stored?
Can you use pre-trained data?
In addition to these questions, you will have to consider whether your model will operate in real-time. If your model is to function in real-time, you will need to create data pipelines to feed the model.
You will also have to consider what form of data is required to build the model. The following are the most common formats in which data is used.
Structured Data: The data will be in the form of rows and columns like a spreadsheet, customer database, inventory database, etc.
Unstructured Data: This type of data cannot be put into rows and columns(or a structure, hence the name). Examples include images, large quantities of text data, videos, etc.
Static Data: This is the historical data that does not change. Consider your call history, previous sales data, etc.
Streaming Data: This data keeps changing continuously, usually in real-time. Examples include your current website visitors.
Based on the problem definition, you need to identify the most relevant data and make it accessible to the model.
Step 3: Preparing The Data
This step is the most time-consuming in the entire model building process. Data scientists and ML engineers tend to spend around 80% of the AI model development time in this stage. The explanation is straightforward – model accuracy majorly depends on the data quality. You will have to avoid the “garbage in, garbage out” situation here.
Data preparation depends on what kind of data you need. The data collected in the previous step need not be in the same form, the same quality, or the same quantity as required. ML engineers spend a significant amount of time cleaning the data and transforming it into the required format. This step also involves segmenting the data into training, testing, and validation data sets.
Some of the things you need to consider at this stage include:
Transforming the data into the required format
Clean the data set for erroneous and irrelevant data
Enhance and augment the data set if the quantity is low
Step 4: Model Building And Training
At this step, you have gathered all the requirements to build your model. The stage is all set and now the solution modeling begins.
In this stage, ML engineers define the features of the model. Some of the factors to consider here are:
Use the same features for training and testing the model. Incoherence in the data at these two stages will lead to inaccurate results once the model is deployed in the real world.
Consider working with Subject Matter Experts. SMEs are well equipped to direct you on what features would be necessary for a model. They will help you reduce the time in reiterating the models and give you a head start in creating accurate models.
Be wary of the curse of dimensionality, which refers to using multiple features that might be irrelevant to the model. If you are using unnecessary features, then the model accuracy takes a dip.
Once you define the features, the next step is to choose the most suitable algorithm. Consider model interpretability when selecting an algorithm. You do not want to end up with a model whose predictions and decisions would be hard to explain.
Upon selecting the appropriate algorithm and building a model, you will have to test it with the training data. Remember, the model will not give the expected result in the first go. You will have to tune the hyperparameters, change the number of trees of a random forest, or change the number of layers in a neural network. At this stage, you can also use pre-trained models and reuse them to build a new model.
Each iteration of the model should ideally be versioned so that you can monitor its output easily.
Step 5: Model Testing
You train and tune the model using the training and the validation data sets respectively. However, the model would mostly behave differently when deployed in the real world, which is fine.
The main objective of this step is to minimize the change in model behavior upon its deployment in the real world. For this purpose, multiple experiments are carried out on the model using all three data sets – training, validation, and testing.
In case your model performs poorly on the training data, you will have to improve the model. You can do it by selecting a better algorithm, increasing the quality of data, or feeding more data to the model.
If your model does not perform well on testing data, then the model might be unable to extend the algorithm. There might be the issue of overfitting where the model is too closely fit with a limited number of data points. The best solution then would be to add more data to the model.
This stage involves carrying out multiple experiments on the model to bring out its best abilities and minimize the changes it undergoes post-deployment.
Step 6: Model Deployment
Once you test your model with different datasets, you will have to validate model performance using the business parameters defined in Step 1. Analyze whether the KPIs and the business objective of the model are achieved. In case the set parameters are not met, consider changing the model or improving the quality and the quantity of the data.
Upon meeting all defined parameters, deploy the model into the intended infrastructure like the cloud, at the edge, or on-premises environment. However, before deployment you should consider the following points:
Make sure you plant to continuously measure and monitor the model performance
Define a baseline to measure future iterations of the model
Keep iterating the model to improve model performance with the changing data
A Note On Model Governance
Model governance is not a defined step in an AI model lifecycle. But it is necessary to ensure the model adapts to the changing environment without many changes in its results.
When a model is deployed in the real world, the data fed to it becomes very dynamic. Apart from the data, there might be changes in the technology, business goals, or a drastic real-world change like a pandemic.
While monitoring the model performance, it is also crucial to analyze how the above changes affect the model. Accordingly, you will have to reiterate the model. Consider monitoring the model for the following parameters:
Deviations from the pre-defined accuracy of the model
Irregular decisions or predictions
Drifts in the data affecting the model performance
Remember, model deployment is only the first step in the AI model lifecycle. You will have to continuously keep iterating the model to keep up with the changes in data, technology, and business.
The above steps gave a detailed approach to building an AI model. However, these steps do not factor in two crucial aspects of a business – time and people.
Like mentioned before, AI models need time to be developed. Even though the efficiency in deploying models has increased, not all companies can deploy efficient models. Most organizations also have a limited number of data scientists and ML engineers. Additionally, a smooth model development involves a combined effort from data engineers, data scientists, ML engineers, and DevOps Engineers.
Considering all these factors, the easy solution would be hiring AI experts who have well-defined processes to build and deploy models at a pace. At Attri, we do just that.
We have a well-defined process to build models which involve all the steps mentioned above. We also create a RACI chart where the role of each person is defined. This helps us to accelerate the model-building process. Additionally, along with the model handover, we provide Knowledge Transfer to our clients so that they can independently manage, monitor, and create multiple iterations of the deployed model.
Every deployed model is delivered with reports of the performance and SOPs to empower our client workforce and democratize AI in their enterprise. You can learn more about our model building expertise here
Students at Delft University of Technology, the Netherlands carried out a crowdsourcing study as part of theCrowd Computing Coursedesigned by Asst. Prof.Ujwal Gadirajuand Prof.Alessandro Bozzonaround one key challenge – the creation and consumption of (high quality) data. Course participants presented several brilliant group projects at the Crowd Computing Showcase event held on 06.07.2021. The group consisting of Xinyue Chen, Dina Chen, Siwei Wang, Ye Yuan, and Meng Zheng was judged to be among the best. The details pertaining to this study are described below.
Background
Saliency mapsare an important aspectof Computer Vision and Machine Learning. Annotating saliency maps, like all data labeling, can be done in a variety of ways; in this case, crowdsourcing was used since it is considered to be one of the fastest methods. The goal was to obtain annotated maps that could be used to acquire a valid explanation for model classifications. Four task designs were used in the experiment.
Method
Preparation
As a first step, an ImageNet-pretrained Inception V3 model was used to extract saliency maps from original images. The maps were subsequently fine-tuned usingCornellLab’s NAbirds Datasetthat contains over 500 images of bird species. 11 of those were selected for the project. SmoothGrad was used to minimize noise levels.
Fig. 1 Example image of a saliency map
Experimental Design
Four types of tasks were used in the course of the experiment: one control task that became the baseline and three experimental tasks. Those three were: training, easy tagging (ET), and training + ET. Each task consisted of 74 images that took approximately three minutes to process. Each saliency map was annotated by three different crowd workers.
Task: Baseline
Three functional requirements had to be met in this part of the experiment:
Instruction – the crowd performers’ understanding of the instructions.
Region selection – the performers’ ability to correctly use the interface tools to mark highlighted areas.
Text boxes – the performers’ ability to use the input boxes appropriately to enter relevant information.
Fig. 2 Baseline interface
Task: Training
The performers were asked to complete a set of training tasks that were designed usingToloka, a crowdsourcing platform. A training pool with three 3-minute tasks was created. The performers had to finish all of the tasks with a minimum accuracy of 70% in order to proceed to the experimental tasks. After this was achieved, the main study began.
Task: Easy Tagging (ET)
As part of the experimental task, the crowd workers had to recognize and label various body parts of bird species. To do that, a picture was provided as a reference. Since the study group’s pilot study demonstrated that color had remained among the most common characteristics, color checkboxes were provided to make color attribute annotations easier for the subjects. In addition, all input boxes contained both “suggestion” and “free input” options, such as when the performers wished to annotate non-color attributes, or the colors provided in the answer box did not match the colors displayed in the image.
Fig. 3 Easy Tagging Interface
Quality Control
Quality control mechanisms were consistent across all four tasks. The performers were asked to use only desktops or laptops during the study to make sure that labeling objects with the bounding boxes was easy and done in the same way throughout. In addition, all of the subjects were required to have secondary education and be proficient in English. Captcha and fast response filtering were used to filter out dishonest workers. The answers were checked manually and accepted based on the following criteria:
At least one bounding box was present.
At least one pair of entity-attribute descriptions was present.
The indices of the bounding boxes had to correspond to the indices of the offered descriptions.
Evaluation Metrics
IOU Score
Intersect Over Union was used to evaluate the accuracy of the bounding boxes. It is calculated by dividing the intersect area of two bounding boxes by the area of the union. The final IOU score is a composite average of multiple IOU values.
Vocabulary Diversity
This metric consists of two values: entity diversity (number of distinct words), and attribute diversity (number of adjectives used to describe one entity).
Completeness
This metric pertains to how complete an annotated saliency map is. It is calculated by dividing the value of the annotated saliency patches by the value of the ground truth annotations.
Description Accuracy
This metric represents a percentage of valid entity-attribute descriptions. The value is calculated by aggregating and averaging the results from three different crowd workers.
Accept rate
This metric is calculated by dividing the number of accepted annotations by the total number of submissions.
Average Completion Time
This metric reflects average duration values of the annotation tasks.
Number of Participants
This metric pertains to the total number of distinct crowd workers participating in the experiment.
Results
The average completion time for all tasks was 3 minutes as predicted.
The mean IOU score was lower in tasks 3 and 4 compared to 1 and 2. This is likely to be the result of the interface differences since the bounding boxes in tasks 3 and 4 contained only one color.
The difference between the mean IOU scores of tasks 1 and 2 is statistically significant (p=0.002) and is in favor of task 2. The difference between the IOU scores of tasks 3 and 4 is not statistically significant (p=0.151).
Training significantly increased completeness (p=0.001). Likewise, easy tagging also raised completeness levels from the baseline values.
No statistically significant difference in entity diversity was observed between tasks 1 and 2 (p=0.829) and tasks 3 and 4 (p=0.439). This was expected since vocabulary diversity was not specifically covered in the training phase.
Training showed to significantly improve description accuracy compared to the baseline values (p=0.001).
Accuracy was increased significantly as a result of the easy tagging interface (p=0.000).
From the within-interface perspective, the difference in attribute diversity of tasks 1 and 2 was statistically significant and in favor of task 1 (p=0.035), which implies that training tends to diminish baseline diversity. No statistically significant differences were observed between the attribute diversities of tasks 3 and 4 (p=0.653).
From the between-interface perspective, a statistically significant difference was observed between tasks 2 and 4 that had different interfaces (p=0.043). This implies that training and interface design are interdependent.
Discussion
Two conclusions can be drawn from this study. One is that performance values depend on what type of interface is being used. In this respect, shortcuts can both help and hinder by either lifting some of the performer’s cognitive load or backfiring and making the performer too relaxed and unfocused. The second conclusion is that training can increase bounding box and description accuracy; however, it can also take away from the subject’s creativity. As a result, requesters have to consider this trade-off before making a decision regarding task design.
Certain limitations of the study should also be taken into account. The most obvious one is that this study should have ideally been conducted as a between-group experiment. Unfortunately, this was not possible. The second limitation is a small number of participants in those tasks that required training. The values received thereafter are likely to be skewed as a result. The last major limitation has to do with applicability – since only aggregated averages from across multiple granularities were used as the final values, these figures are not likely to accurately represent most non-experimental settings.
Since one of the findings suggests that input shortcuts can both increase accuracy and concurrently diminish creativity, future studies should look at different study designs with multiple shortcuts (e.g. shape and pattern). In this scenario, the negative side effect of decreased creativity and boredom may be countered with the more sophisticated interfaces that are practical and user-friendly. Finally, the authors propose a switch from written to video instructions as these will likely be more effective and result in a greater number of subjects finishing the training phase.
Project in a nutshell
Saliency maps are an integral part of ML’s advance towards improved Computer Vision. On par with other forms of data labeling, annotating saliency maps is at the core of training models and their classification. Using crowd workers from Toloka and a dataset of birds from CornellLab’s NABirds, this paper examined how crowdsourcing can be used in saliency map annotations. To do so, four types of tasks were used, of which one became the baseline, and the other three—training, easy tagging (ET), and training/ET—were the main tasks. All of the crowd performers were recruited from theToloka crowdsourcing platform. Several metrics were used for evaluation, including IOU score, vocabulary diversity, completeness, accuracy, accept rate, and completion time among others. Results showed that the choice of interface had a major effect on performance. In addition, training increased the bounding box as well as description accuracy but also diminished the subjects’ creativity. Implications of these findings and suggestions for future studies are discussed.
Recognition of employees has long been a cornerstone of good administration. However, as the battle for talent heats up, how businesses appreciate their employees is more crucial than ever.
Businesses gain from employee recognition in a variety of ways. Employees who believe their job is recognized and valued will have a greater sense of purpose and will be more engaged and productive.
HR departments may struggle to execute a successful employee appreciation program if they don’t have the necessary resources. Manually tracking results is time-consuming, and HR departments must mix this obligation with other responsibilities and tasks, some of which can be automated.
Fortunately, when organizations are aware of their objectives and have a well-defined company culture, the problems of integrating the correct technology can be lessened.
Importance of Employee Recognition:
Employees can understand that their organization values them and their contributions to the success of their team and the firm as a whole when they receive recognition. This is especially important as organizations expand and evolve. It helps employees feel secure in their worth to the organization, which motivates them to keep doing exceptional jobs.
For organizations, happier employees mean more harmony in the workplace, as well as higher retention rates. And, because these people are more committed and productive, your company’s income will rise.
Role of Technology in Employee Recognition:
Recruiting, onboarding, training, and administering leaves are just a few of the responsibilities of the HR department. They may automate manual activities with the correct software, giving them more time to focus on the important areas of their job. Screening software, for example, can help firms save time when it comes to calculating PTO, scheduling, and evaluating applicants’ CVs.
HR will be able to devote more time to more important responsibilities as a result of the added time. Teams can also use technology to digitize their wellness programs and reward programs. Employees will be able to gain a better understanding of their performance and determine how to achieve their objectives more rapidly as a result.
HR departments can use software to improve employee recognition activities beyond simply congratulating employees at the next team meeting. Companies can track their employees’ performance using segmented data with the correct tools.
Technology allows firms to cultivate employee-to-employee appreciation in addition to employer-to-employee acknowledgment. When someone performs well, for example, team members can offer badges and compliments. Employees are motivated to do their best for the team and your company when they receive favorable feedback.
HR Tech in Employee Recognition Program:
Gamification –Gamification is a strategy for improving systems, services, organizations, and activities in order to generate experiences similar to those found in video games in order to encourage and engage users.
HR departments have a lot of alternatives when it comes to gamifying an employee recognition program. One possibility is to construct reward levels, with employees earning experience points (XP) as they go. They earn a prize if they reach a particular point.
Quizzes and videos can also be used to make employee appreciation program more gamified.
Tracking Employee Performance –Employees generate complicated data at work, and technology is required to fully comprehend employee performance. HR technology makes it easier to track how close employees are to meeting their key performance indicators (KPIs) and to see how far they’ve come in their time with the organization.
Team managers can also leave feedback using HR technology. While they can do so in real-time chats, keeping a record implies that employees and other management can access this information anytime they need it.
Automate Tasks That Waste Time –Every day, employees squander a significant amount of time on unproductive jobs. Two culprits are pointless meetings and emails.
Both of these issues are simple to resolve for HR personnel. Unnecessary meetings can be avoided with the use of communication technology such as Slack and Skype. Automating boilerplate memos and emails, as well as background checks, payrolls, and even recruiting recruits, is a fascinating possibility.
HR processes have become a lot simpler thanks to technological advancements. Teams can use automation to focus on higher-level projects that require more attention.
Teams can also use HR technologies to decide who deserves to be rewarded. Because managers and HR staff can see the output for each employee, digitizing incentives programs removes any possible partiality.
Teams that integrate technology into their employee recognition plan will, in the end, be able to acknowledge achievement more effectively—and earn happier, more engaged employees as a result.
ABOUT THE AUTHOR: ADVANTAGE CLUB
Advantage Club is a global provider of employee benefits. The platform serves to digitize all employee demands under one canopy through numerous employee engagement programs such as incentives, rewards & recognition, flexible and tax-saving modules. It now serves over 300 organizations in 70 countries and has over 10,000 brand partnerships.
The finance industry generates a huge amount of data. Did you know big data in finance refers to the petabytes of structured and unstructured information that helps anticipate customer behaviors and create strategies that support banks and financial institutions? The structured information managed within an organization enables providing key decision-making insights. The unstructured information offers significant analytical opportunities across multiple sources leads that leads to increasing volumes.
The world generates a staggering 2.5 quintillion bytes of data every single day! Seeing the abundance of data we generate, most businesses are now seeking to use this data to their benefit, including the banking and finance sector. But how can they do that? With big data, of course. Here are some of its many benefits in the context of banks to help you better understand.
Monitor customer preferences: Banks have access to a virtual goldmine of highly valuable data that is largely generated by customers themselves. As a result, financial institutions have a clear insight about what their customers want, which allows them to offer them better services, products, etc. that are in sync with their requirements.
Prevent fraud: Since these systems typically involve the use of high-grade algorithms and analytics, banks can benefit immensely in the risk department. This is because such systems can identify even possibly fraudulent activities and deter malicious activities.
Allow us to now walk through some of the key challenges:
Legacy systems: The mind-boggling amount of data involved in banking operations can easily stress out a bank’s legacy systems. This is why experts recommend upgrading one’s systems before integrating big data.
Data quality management: Outdated, inaccurate, and incomplete data poses grave challenges, often spoiling the results of analytics, etc. Hence, banks must adopt processes to ensure data is reviewed before it enters the system.
Consolidation: Banks add a humongous amount of data to their databases every single day, which is then channeled into different systems for better use. However, this can result in data silos and prevent the free flow of data within systems and teams. Hence, it is important to consolidate data immediately.
Finally, let us also take a look at some of its use cases:
Enhanced user targeting: It is abundantly clear that big data can help banks understand their customers better among other things. One key way to use such insights is by applying them to marketing campaigns, ensuring they are better targeted and thus, primed to deliver better results.
Tailored services: It is not news that today’s customers are, well, extremely finicky and demanding. Now, to win them over and ensure their loyalty, banks are putting big data to work so they can better understand customers, their requirements, etc. This information is then used to tailor the company’s offerings and services to achieve better sales and business results.
Better cybersecurity: Given the abundance of data security risks and threats this sector faces every single day, it ought to come as no surprise that banks are now turning to big data for help. It typically involves the use of real-time machine learning along with predictive analytics on big data to identify risky behavior, reduce risk, etc.
There is not even a shred of doubt that digital transformation in the finance and banking sector has had a significant impact on the world. Thankfully, save for a few challenges, most of these changes have been for the better of, first, the customers and, then, the companies as well. To cut a long story short, any business in this sector that even hopes to thrive will do well to embrace big data and leverage its countless benefits for the business’ better future.
Machine learning models make use of training datasets for predictions. And, thus labeled data is an important component for making the machines learning and interpret information. A variety of different data are prepared. They are identified and marked with labels, also often as tags, in the form of images, videos, audio, and text elements. Defining these labels and categorization tags generally includes human-powered effort.
Machine learning models which fall under the categories of supervised and unsupervised, pick the datasets and make use of the information as per ML algorithms. Data labeling for machine learning or training data preparation encompasses tasks such as data tagging, categorization, labeling, model-assisted labeling, and annotation.
Machine Learning Model Training
The majority of effective machine learning models use supervised learning, which uses an algorithm to translate input into output. Machine learning (ML) industries, such as facial recognition, autonomous driving, drones, and require supervised learning. And as a reason their reliability on the labeled data increases. In supervised learning, sometimes, machine learning models can also work to predict loss reduction. This instance is referred to as empirical risk minimization. For preventing such scenarios, data labeling and quality assurance must be vigorous.
In machine learning, as a norm, there are three main types of data sets that are utilized – dimensionality, sparsity, and resolution. And the data structure can also vary depending on the business problem. Textual data can be based on records, graphs, and order, etc. The human-in-the-loop uses labels to identify and mark predefined characteristics in the data. If the ML model requires to predict accurate results and also develop a suitable model, the dataset quality must be maintained. For example, labels in a data set identify whether the image has objects like a cat or a human, and also pinpoint the shape of the object. In a process known as “model training,” the machine learning model employs human-provided labels to understand the underlying patterns. As a result, you’ll have a trained model that you can use to generate predictions and develop a customized model based on fresh data.
Use Cases of Data Labeling in Machine Learning
Several use cases and AI tasks pertaining to computer vision, natural language processing, and speech recognition, computational instances need appropriate forms of data labeling.
1. Computer Vision: To produce your training dataset for a computer vision system, you must first label images, pixels, or key spots, or create a bounding box that completely encloses a digital image. Once the annotation is done, a training data set is produced and the ML model is trained depending on it.
2. Natural Language Processing: To create your training dataset for natural language processing, you must first manually pick key portions of text or tag the text with particular labels. Tag and justify labels in the text for the training dataset. Sentiment analysis, entity name identification, and optical character recognition or OCR are all done using natural language processing approaches.
3. Audio Annotation: Audio annotations are used for machine learning models which use sounds in a structured format for example – extraction of audio data and tags. NLP approaches are then applied to tagged sounds to interpret and obtain the learning data.
Maintaining Data Quality and Accuracy in Data Labeling
Normally, the training data is divided into three forms – training set, validation set, and testing set. All three forms are crucial for learning the model. Gathering the data is an important step to collating raw data and properly defining the attributes, in order to get them labeled.
Machine learning datasets must be accurate and of high quality. Accuracy refers to how accurate each piece of data’s labeling is in comparison to the business problem and what it aims to solve. Equally crucial are the tools which are used for labeling or annotation of data. AI platform data labeling services form the core for developing dependable ML models for artificial intelligence-based programs.
The company has set the industry standard for quality and on-time delivery of AI and ML training data by partnering with world-class organizations. Cogito is well known in the AI community for providing reliable datasets for various AI models as the company fully supports data protection and privacy legislation. Cogito provides the clients with complete data protection rights that are governed by the norms and regulations of a GDPR and CCPA, ensuring total data privacy.
