Course Tracks
Model deployment, monitoring, version control, and securing the full ML lifecycle. Built by an engineer who runs ML systems in production.
Courses in this track
Deploy models at scale with real serving infrastructure. BentoML, Ray Serve, TorchServe, and Kubernetes-native patterns for production ML.
Deploy models at scale with real serving infrastructure. BentoML, Ray Serve, TorchServe, and Kubernetes-native patterns for production ML.
MLflow, DVC, and Weights & Biases used by real ML teams. Version data, code, and models together so experiments are reproducible.
Statistical tests, data quality checks, and performance monitoring that catch degradation before it impacts users. Build monitoring into models from day one.
Feast, Tecton, and custom feature stores. Build feature pipelines that work consistently across training and serving without silent inconsistencies.
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