CML is a project to help ML and data science practitioners automate their ML model training and model evaluation using best practices and tools from software engineering, such as GitLab CI/CD (as well as GitHub Actions and BitBucket Pipelines). The idea is to automatically train your model and test it in a production-like environment every time your data or code changes.
In this talk, you'll learn how to:
- Automatically allocate cloud instances (AWS, Azure, GCP) to train ML models. And automatically shut the instance down when training is over
- Automatically generate reports with graphs and tables in pull/merge requests to summarize your model's performance, using any visualization library
- Transfer data between cloud storage and computing instances with DVC
- Customize your automation workflow with GitLab CI/CD
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