- CI refers to automatically testing and validating code as well as data and the model components of an ML project, and merging them into a single version. This ensures that the changes made by different data scientists working collaboratively on an ML project integrate well and do not break the model in production.
- CD refers both to continuous delivery and continuous deployment. In continuous delivery, changes to a project component are automatically uploaded or delivered to a central repository after they have been tested and validated. With continuous deployment, these changes in the central repository are automatically published to be accessed by the end user.
To access this webinar, please register here: https://hubs.li/Q01pXrSH0
Topic: “CI/CD for Machine Learning”
Speaker: Alex Kim, Solutions Engineer at Iterative
His background is in physics, software engineering, and machine learning. In the last couple of years, he became increasingly interested in the engineering side of ML projects: processes and tools needed to go from an idea to a production solution.
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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Hope will be usefull.