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Course of Continuous Delivery For Machine Learning: pipeline

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Course summary

  • Discover how to use Continuous Delivery applied to Machine Learning
  • Explore best practices and tools of Continuous Delivery
  • Use a complete pipeline for Machine Learning
  • Learn how to cope with training test and deploy of models within the build pipelines
  • Learn how to use tools for monitoring and artifact tracking
  • Manage the development cycle of Machine Learning

Target Audience

Those willing to learn how to automate pipelines for machine learning builds and developers, data engineers, data scientist, software engineers and everyone else involved with the technical aspects of building the software.

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Last update

30/05/2023

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8h

To conclusion

50

Activities

120

Minutes of video

5

Students in this course

Certificate of participation

Instructors

Detailed content

  1. Configuring the application

    • Docker
    • Log, terminal, and stop
    • Docker Compose

  2. Understanding the application

    • Serving the model

  3. Modifying the application

    • Running experiments
    • Continuing with experiments

  4. Testing the application

    • Testing and delivering
    • Everything continuous

  5. Monitoring the application

    • Monitoring and observability

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