Weights and Biases
A popular MLOps platform for experiment tracking, model monitoring, dataset versioning, and collaboration in machine learning development.
Why It Matters
W&B brings software engineering discipline to ML development. It makes experiments reproducible, comparable, and collaborative.
Example
Logging every training run's hyperparameters, metrics, and artifacts to W&B, then comparing 50 experiments side-by-side to identify the best configuration.
Think of it like...
Like a lab notebook for data scientists — every experiment is meticulously recorded so you can trace back exactly what you did and why.
Related Terms
MLOps
Machine Learning Operations — the set of practices that combine ML, DevOps, and data engineering to deploy and maintain ML models in production reliably and efficiently.
Model Monitoring
The practice of continuously tracking an ML model's performance, predictions, and input data in production to detect degradation, drift, or anomalies after deployment.
Hyperparameter Tuning
The process of systematically searching for the best combination of hyperparameters for a model. Since hyperparameters are set before training, finding optimal values requires experimentation.