This series of articles details the creation of a machine learning pipeline on Kubernetes. Part 3 focuses on deploying model inference services using FastAPI and MLflow, building upon the CI-driven model training established in Part 2. The earlier installment covered training models with Jenkins, MLflow, and DVC, laying the groundwork for the subsequent deployment phase. AI
IMPACT Details infrastructure for deploying and training ML models, relevant for MLOps engineers.
RANK_REASON The cluster describes a technical tutorial series on building an ML pipeline, which falls under research and development rather than a frontier release or significant industry event.
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