This Weights & Biases (W&B) MLOps course introduces modern machine learning operations practices focused on improving experimentation, reproducibility, and model management in real-world AI systems.
The course begins with an introduction to machine learning for business decision optimization, showing how ML can be applied to solve practical decision-making problems. It then explores CI/CD concepts for machine learning, explaining how automation improves reliability and scalability in ML workflows.
A key focus of the course is experiment tracking using Weights & Biases, where you learn how to log, visualize, and reproduce machine learning experiments efficiently. This ensures that every model training run is fully traceable and comparable.
You will also learn how to use the W&B Model Registry to manage and organize models across teams, making it easier to deploy and maintain production systems. The course further introduces advanced tools like W&B Tables and Reports for exploratory data analysis and model evaluation.
Additional topics include hyperparameter tuning using W&B Sweeps, helping you optimize model performance automatically. You will also explore critical concepts such as data leakage detection and error analysis to improve model reliability.
By the end of this course, you will understand how to build structured, reproducible, and production-ready ML workflows using Weights & Biases tools and modern MLOps practices.
