Course Highlights
  • Design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment requirements.
  • Establish a model baseline, address concept drift, and prototype how to develop, deploy, and continuously improve a productionized ML application.
  • Build data pipelines by gathering, cleaning, and validating datasets. Establish data lifecycle by using data lineage and provenance metadata tools.
  • Apply best practices and progressive delivery techniques to maintain and monitor a continuously operating production system.
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Machine Learning Engineering for Production (MLOps) Specialization

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