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Welcome to 8-Chems: Data Science, AI and Software Development, Step by Step

Chemseddine Berbague
Welcome to 8-Chems , a blog where I explain data science, AI, and software development step by step. Imagine opening a tutorial and finding exactly what you wished a colleague had told you: the intuition first, then a small example, then the formula or the code, and finally how it all fits into a real project. That is the kind of post I try to write here. Who writes here I'm Chemseddine Berbague, a data scientist and developer. I earned my Ph.D. in computer science in 2021, working on recommender systems and multi-objective optimization, and I enjoy turning research ideas into things that run in practice: forecasting models, optimization methods, data pipelines, and web applications. This blog is my notebook in public. If a topic took me a while to understand, I write it down in the way I would have liked to read it. What you will find The posts fall into four themes. Pick the one that matches what you are looking for. Data science in retail and forecasting A series about ...
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GCP: Demo of testing Airflow, MLflow, XGBoost

Chemseddine Berbague
Training a machine-learning model on a laptop is the easy part. The hard part starts the day after: who reruns the training when new data arrives? Where are the experiments recorded? How does a better model reach production without someone copying files around, and how do you know it is actually better? Those questions are what MLOps is about. In this tutorial we will build, step by step, a small but complete MLOps platform on Google Cloud. The machine-learning model itself (an XGBoost classifier on the public UCI Bank Marketing dataset) is deliberately boring: it is only a vehicle. The real subject is everything around it: orchestrating, tracking, deploying, monitoring and reproducing an ML workflow. The context: what we are building and why Imagine a bank that wants to predict whether a customer will subscribe to a term deposit after a marketing call. A data scientist can train such a model in ten minutes. A team that has to retrain it every week, compare each new m...
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