As cloud services become more and more popular, the potential for building machine learning applications in the cloud also increases. In this blog post, we'll focus on Amazon Web Services (AWS) and explore how to set up a fully automated data pipeline for a machine learning application using some of these services.

The concept of the marketplace has not changed drastically over time, but the arrival of popular online e-commerce platforms such as Amazon, Ebay, and AirBnB innovated how business is done in unmatched ways.

The previous article taught us how we can easily deploy Serverless applications using the Serverless framework and a basic understanding of the AWS environment. Here, we will learn about AWS Cloud Development Kit (CDK), use it to create AWS resources, and try AWS Cognito - an identity provider.

Have you ever wanted to use Redux, but you didn’t because it requires too much boilerplate code? Configuring a Redux store is complicated and you need several packages to get Redux to work with React. Now, there is a much easier and faster way to integrate Redux into your React project: Redux Toolkit. We will cover the first part of the story in this article. In part II, we will go deeper into using Redux Toolkit and its latest addition: RTK Query.

So, we talked about how we can use neural networks to build a face recognition system. Then we talked about word embeddings, a method for representing textual data. In this article, we are going to talk about all the steps in a typical machine learning project, from start to finish. Are you ready? “Yeah!”—you shout. Let’s go!

Just think of the number of objects that Google Maps has to store to support everything we do while using the application - all the buildings in the world, all the roads, rivers, terrain information, traffic… On the other hand, users will often expect instant or close to instant responsiveness of the application while moving on the map and zooming in and out. So, how do we accomplish that?