A 6-week hands-on masterclass in production MLOps engineering. Build a file-backed experiment tracker, a containerized model registry, an inference server with dynamic batching, a cached pipeline DAG, automated drift detectors (PSI/KS), and high-performance LLM serving infra (KV-cache/continuous batching) from scratch.
docker registry tracking packaging containers cicd inference-server machine-learning-production mlops model-registry data-drift llm llm-serving llm-deployment cicd-pipelines devops-for-ml mflow-alternative
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Updated
May 27, 2026 - Jupyter Notebook