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MLOps Essentials: Model Deployment and Monitoring

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Machine learning operations (MLOps) is one of the fastest growing subfields of artificial intelligence. As more and more models have been deployed in production, the need for a structured, agile, end-to-end, automated machine learning lifecycle has continued to grow. In this course, instructor Kumaran Ponnambalam shows you how to apply key concepts from MLOps to create structured, improved outcomes in your everyday workflow.

Explore the fundamentals of MLOps to get up and running on your next machine learning project. Find out why so many data scientists, engineers, and project managers are so excited about ML models, as you discover the ins and outs of successfully deploying and monitoring models on your own. From continuous delivery to model serving and continuous monitoring to drift management, Kumaran equips you with the skills you need to start practicing effective, fair, explainable, and responsible artificial intelligence.


01 - Introduction
  • 01 - Getting started with MLOps
  • 02 - Course coverage
  • 03 - Review of MLOps lifecycle

  • 02 - 1. Continuous Delivery
  • 01 - An ML production setup
  • 02 - Deployment pipelines
  • 03 - Deployment rollout strategies
  • 04 - Planning for infrastructure
  • 05 - Deployment best practices
  • 06 - Tools and technologies for deployment

  • 03 - 2. Model Serving
  • 01 - Model serving patterns
  • 02 - Scaling model serving
  • 03 - Building resiliency in serving
  • 04 - Serving multiple models
  • 05 - Tools and technologies for serving

  • 04 - 3. Continuous Monitoring
  • 01 - The monitoring pipeline
  • 02 - Instrumentation for observability
  • 03 - Metrics to monitor
  • 04 - ML production data best practices
  • 05 - Alerts and thresholds for ML
  • 06 - Tools and technologies for monitoring

  • 05 - 4. Drift Management
  • 01 - Introduction to model drift
  • 02 - Concept drift basics
  • 03 - Managing concept drift
  • 04 - Feature drift basics
  • 05 - Managing feature drift

  • 06 - 5. Responsible AI
  • 01 - Elements of responsible AI
  • 02 - Explainable AI
  • 03 - Fairness in ML
  • 04 - Security of ML assets
  • 05 - Privacy in machine learning

  • 07 - Conclusion
  • 01 - Continuing on with MLOps
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    شناسه: 1639
    حجم: 153 مگابایت
    مدت زمان: 84 دقیقه
    تاریخ انتشار: 27 دی 1401

    45,900 تومان
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