Azure Data Science services empower data scientists and developers with a wide range of productive experiences to build, train, and deploy machine learning models and foster team collaboration.

Topics Covered

Implement an early end-to-end integration to iterate faster by using small and valuable increments. Create an entire deployment process, continuous monitoring, and management strategy.


  • Evaluate the best approach to train and serve models to production – batch / real-time.
  • Adopt ML System Architecture and Writing Production-Ready Codes.
  • Model Deployment, Monitoring, Maintenance Measures – Health Checks, Data Drifts.
  • MLOps for implementing and automating continuous integration (CI), continuous delivery (CD), and continuous training (CT) for machine learning (ML) systems.
  • Implement feedback loops to improve the model’s performance.


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