Miscommunication and Dependency Management Issues Photo by Alexander Dummer on Unsplash Although almost everyone recognizes the significance of data science and machine learning, it does not come on a platter of gold. According to a 2019 article by Venturebeat, roughly 87 percent of data science projects never make it into…

11 min to read Machine Learning and data science life cycle involved several phases. Each phase requires complex tasks executed by different teams, as explained by Microsoft in this article. To solve the complexity of these tasks, cloud providers like Amazon, Microsoft, and Google services automate these tasks that speed…

If you have a little idea of machine learning and data science you must have heard the word “feature”. In this blog post, we define feature store explain why we need feature store components of feature store compare feature store and other data storage feature…

Building and training a machine learning model with good accuracy or performance is not enough. A Machine learning model adds more value when deployed to production and is helpful to everyone, especially people who are not in the machine learning field. Deploying an ML model to production involves a…

A step-by-step guide — Several steps are involved in deploying a machine learning model to production, but these steps can be complicated and expensive. This article focuses on the most straightforward and easiest way to deploy a machine learning model to production for free by using Streamlit. Streamlit provides a way to build your…

Deciding on the database to use for our application or workload can be very tricky. Since I join AWS Community Builder, I spend at least 1 hour every day exploring AWS based on use cases. Amazon Web Service(AWS) provides several options for databases; we can be confused about the right…

The word Serverless is becoming a popular word in cloud computing. This does not necessarily mean we don’t use a server, instead it means we don’t need to “provision” that is create the underline infrastructure which is most time a server required to run your code. The cloud provider…

6 min to read Data Stream Analytics also called event stream processing or real-time analytics is the processing and analysis of real-time data. These streaming data could be transaction data from an e-commerce website, financial trading floors, telemetry from IoT devices, and social media data. This article gives a brief…

In my first article in this series Introduction to Big Data Technologies 1: Hadoop Core Components, I explained what is meant by Big Data, the 5 Vs of Big Data, and brief definitions of all the major components of the Hadoop ecosystem. In this article, we will be diving into…