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MLOps Essentials: Model Development and Integration

سرفصل های دوره

Machine Learning Operations (MLOps) is a fast-growing domain the field of AI. As more models are deployed in production, the need for a structured, agile, end-to-end ML lifecycle with automation has grown multifold. MLOps provides structure to machine learning projects and help them succeed over the long run. In this course, instructor Kumaran Ponnambalam focuses on the key concepts of MLOps and helps you apply these concepts to your day-to-day ML work. Kumaran introduces you to the machine learning life cycle and explains unique challenges with ML, as well as important definitions and principles. He walks you through the requirements and design for ML projects, then dives into data processing and management. Kumaran explains various tools and technologies that you can use in the automation and management of continuous training. He covers best practices for model management, then offers detailed instruction on continuous integration.


01 - Introduction
  • 01 - Getting started with MLOps
  • 02 - Scope and prerequisites

  • 02 - 1. Introduction to MLOps
  • 01 - Machine learning life cycle
  • 02 - Unique challenges with ML
  • 03 - What is DevOps
  • 04 - What is MLOps
  • 05 - Principles of MLOps
  • 06 - When to start MLOps

  • 03 - 2. Requirements and Design
  • 01 - Selecting ML projects
  • 02 - Creating requirements
  • 03 - Designing the ML workflow
  • 04 - Assembling the team
  • 05 - Choosing tools and technologies

  • 04 - 3. Data Processing and Management
  • 01 - Managed data pipelines
  • 02 - Automated data validation
  • 03 - Managed feature stores
  • 04 - Data versioning
  • 05 - Data governance
  • 06 - Tools and technologies for data processing

  • 05 - 4. Continuous Training
  • 01 - Managed training pipelines
  • 02 - Creating data labels
  • 03 - Experiment tracking
  • 04 - AutoML
  • 05 - Tools and technologies for training

  • 06 - 5. Model Management
  • 01 - Model versioning
  • 02 - Model registry
  • 03 - Benchmarking models
  • 04 - Model life cycle management
  • 05 - Tools and technologies for model management

  • 07 - 6. Continuous Integration
  • 01 - Solution integration pipelines
  • 02 - Notebook to software
  • 03 - Solution integration patterns
  • 04 - Best practices for solution integration

  • 08 - Conclusion
  • 01 - Continuing on with MLOps
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    مدت زمان: 91 دقیقه
    تاریخ انتشار: 27 دی 1401
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