Feature Stores (Overview)

Summary

Centralized systems for storing, sharing and serving ML features consistently between training and inference — solves the common bug where training and serving features silently drift apart.

How to Learn This

1Read how a feature store keeps offline (training) and online (serving) features consistent.
2Understand the 'training-serving skew' problem a feature store is designed to prevent.
3Explore an open-source feature store (e.g. Feast) at a conceptual level.

Kubernetes for ML (Overview)

Deploy a Model as an API

More topics in MLOps & Model Deployment

Model Serving (FastAPI/Flask)Docker for ML Models Model Versioning Experiment Tracking (MLflow, Weights & Biases)CI/CD for ML Pipelines Model Monitoring & Drift Detection A/B Testing Models Batch vs Real-time Inference Kubernetes for ML (Overview)

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