Learning from Zoomcamps at DataTalks.Club

I recently finished Module 2 of the MLOps Zoomcamp (hands-on with experiment tracking and model management). The homework was intense – a real grind – but very educational. Rather than sifting through disorganized files for metrics and models, we used MLflow to automatically log and organize all experiment runs. Hyperopt handled our search space, and the best model got neatly registered. Below I share how each step helped turn chaotic experimentation into a clear, reproducible process. Experiment Tracking with MLflow Experiment tracking is about systematically recording every training run so you can reproduce and compare results. MLflow makes this easy. In practice we wrapped our training code (in train.py ) with MLflow’s run API and enabled MLflow’s autologging ( mlflow.sklearn.autolog() ). This meant every model parameter, metric, and artifact was captured automatically. For example, once MLflow autologging was on, we track all hyperparameters and metrics without manual logging . Wit...

Introduction A few months ago, I completed the  Machine Learning Zoomcamp  by DataTalksClub—an intensive five-month journey that transformed me from a curious novice to someone confident in building, evaluating, and deploying machine learning models. But as I soon discovered, the real world of production-grade AI isn’t just about training a high-accuracy model. It’s about ensuring that model survives—and thrives—in the chaotic, ever-changing landscape of real-world data. This realization led me to enroll in DataTalksClub’s  MLOps Zoomcamp , a course designed to tackle the very challenges that kept me awake after my first foray into ML. In this blog post, I’ll share why I’m diving into MLOps, the gaps it fills in my knowledge, and what I hope to achieve through this journey. From Notebook to Production: The Challenges The ML Zoomcamp taught me the fundamentals about machine learning and machine learning engineering including deployment of the trained models. But if these...