Suvendu Mohanty changed from software to ML engineering before the AI boom. Here's how he made the switch — and his advice ...

Learn how to organize and structure your machine learning projects for real-world deployment. From directory layout to model versioning, data pipelines, and CI/CD integration — this guide will ...

Google Colab is a really handy tool for anyone working with machine learning and data stuff. It’s free, it runs in the cloud, and it lets you use Python without a lot of fuss. Whether you’re just ...

These steps often stall machine learning projects between experimentation and production because of a lack of engineering resources or the complexity of debugging pipelines.

Typical Azure Machine Learning Project Lifecycle (source: Microsoft). At the upcoming Visual Studio Live! @ Microsoft HQ 2025 conference in Redmond, Eric D. Boyd, founder and CEO of responsiveX, will ...

Figure 1: Workflow of a Machine Learning Project Building an Observable ML Pipeline This article will demonstrate an ML pipeline and its observability for real-world credit card fraud detection.

Setting Up a Machine Learning Environment on Linux To begin AI/ML development on Linux, follow these steps: Choose a Linux Distribution: Ubuntu, Debian, Fedora, and Arch Linux are popular choices for ...

Machine learning is a multibillion-dollar business with seemingly endless potential, but it poses some risks. Here's how to avoid the most common machine learning mistakes.