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Build the complete pipeline: from raw data ingestion to a production-serving model, with automated data versioning on Kubernetes. Five sequential guides covering every step.
These guides build on each other. Start with Step 1 and work through to the end.
Ingest raw data from PostgreSQL or APIs, validate with Great Expectations, store partitioned Parquet in S3, orchestrate with Airflow, and version with DVC.
MLOps PipelineClean and impute missing values, engineer features from raw events, split correctly without leakage, handle class imbalance, and integrate with Feast Feature Store.
MLOps PipelineUnderstand training-serving skew, offline vs online stores, and how to deploy Feast with Redis and PostgreSQL on Kubernetes so every model consumes features in the exact order it was trained on.
MLOps PipelineWrite a production training script, track experiments with MLflow, tune hyperparameters with Optuna, implement evaluation gates, and run training as Kubernetes Jobs.
MLOps PipelineDeploy models to Kubernetes with KServe InferenceService, implement canary deployments with traffic splitting, configure autoscaling, and set up Prometheus monitoring.
MLOps PipelineAutomate dataset versioning end-to-end: deploy Airflow with KubernetesExecutor on EKS, configure Pod Identity for S3 access, and run a three-task DAG that pulls, modifies, and version-commits your dataset automatically.
MLOps PipelineMulti-stage builds, base image selection, dependency pruning, and a real Kubeflow case study showing 89% size reduction.