Sabrine Bendimerad

A Practical Guide to Applying Probability Concepts with Python in Real-World Contexts Sabrine Bendimerad · Follow Published in Towards Data Science · 13 min read · 7 hours ago — Source Introduction Tired of Guessing About The Future? 🤔 You’re in the right place! My name is Sabrine. I’m an applied mathematics engineer working in AI for 10 years, and I struggled in my early experiences to bring the power of probability into the real

A Step-by-Step Guide to Making Data-Driven Decisions with Practical Python Examples Sabrine Bendimerad · Follow Published in Towards Data Science · 10 min read · 10 hours ago — Source Have you ever wondered if a change to your website or marketing strategy truly makes a difference? 🤔 In this guide, I’ll show you how to use hypothesis testing to make data-driven decisions with confidence. In data analytics, hypothesis testing is frequently used when running

How the market has changed (and the roadmap I’d follow if I started my journey today) Sabrine Bendimerad · Follow Published in Towards Data Science · 11 min read · 9 hours ago — Source This article is based 100% on my experience, my research, and what I’m observing in the European market. Things might look slightly different in the rest of the world, but we’re all facing the same waves of change in the

A Complete Guide to Serving a Machine Learning Model with FastAPI Sabrine Bendimerad · Follow Published in Towards Data Science · 10 min read · 14 hours ago — pixabay.com Welcome to this third article in my MLOps series. In the first article, we explored Docker and how it simplifies application packaging. In the second article, we managed machine learning models using MLflow, Azure, and Docker. Now, in this third part, we’ll bring everything together

A guide to tracking experiments and managing models Sabrine Bendimerad · Follow Published in Towards Data Science · 10 min read · 1 hour ago — pixabay.com In the first article, we explored Docker’s powerful ability to package applications and their dependencies into portable containers, ensuring consistency across various environments. Building on that foundation, this article introduces MLflow, an important tool for experiment tracking and model management in machine learning workflows. We will demonstrate how