How to apply software engineering good practices when developing machine learning systems — As an ML engineer, you are still a software engineer specializing in building AI/ML systems. Therefore, you still have to write clean, maintainable, and scalable software. I want to take a software engineering approach in this article when looking at ML applications. What do software engineers love most?…

Unlock the full potential of your data with a deep understanding of these fundamental statistical concepts — As a data scientist, it is essential to have a strong foundation in statistical concepts and methods. These concepts and methods provide the tools and techniques necessary for analyzing and interpreting data, making informed decisions, and communicating results effectively. In this blog, we will explore the top 10 most interesting…

Handle Data Leakage. Reduce Labeling Costs. Decrease Computation Time and Expenses. — Data integrity is one of the biggest concerns for companies and engineers in the latest period. The amount of data we have to process and understand only gets more significant, and manually looking at millions of samples is not sustainable. …

Explaining Lifespan by David Sinclair to a 7-years old. — The purpose of this article is to document my key takeaways from [1] Lifespan by David Sinclair. The book completely changed my perspective on aging and life in general. I don’t usually write about this topic, but it contains valuable information that everybody should know. Disclaimer: I am not a…

Compose all the components from Scikit-Learn Pipelines to build custom production-ready models — This tutorial will teach you how and when to use all the advanced tools from the Sklearn Pipelines ecosystem to build custom, scalable, and modular machine learning models that can easily be deployed in production. In isolation, there is much content about different components from the Sklearn Pipelines toolbox. I…

An end-to-end tutorial on how to build a churn prediction pipeline using only Apache Spark. — This article is a tutorial on how to build a churn prediction classifier using the ML stack from Spark. We will use data from a fictive company called Sparkify, a music streaming company. The dataset contains all kinds of events created by the users who interacted with the platform. We…

Understand the intuition behind the Singular Value Decomposition (SVD) algorithm. — I am writing this article because, at first, I struggled to understand the mathematical intuition behind the SVD algorithm. But if you throw away all the fancy math terms, the idea behind the algorithm is easy to understand. After reading this article, you will get an intuition about why and…

7 principles to quickly bring MLOps to your machine learning projects. MLOps is a new field that was born due to the high failure rate of most machine learning projects. By failure, we understand the inability to generalize your freshly cooked model and ship it into a production environment to…