ML Pipeline Engineering
Building automated systems that manage the full machine learning lifecycle, from data ingestion and model training to deployment, monitoring, and retraining.
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Why It Matters
Most ML models never reach production due to pipeline failures. Engineers who build reliable, scalable MLOps infrastructure turn experimental models into business-critical systems.
How to Get Started
Learn MLOps tools like MLflow, Kubeflow, or Vertex AI Pipelines. Study CI/CD for ML with tools like DVC and practice building end-to-end pipelines that automate training, evaluation, and deployment.
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