Machine learning (ML)is maturing as a discipline: now that it is trivially easy to create and train models, it has never been more challenging to manage the complexity of experiments, changing datasets, and the demands of a full-stack project. This talk examines why one of the staples of DevOps, continuous integration, has been so challenging to implement in ML projects so far and how it can be done using open-source tools like Git, GitHub Actions, and DVC (Data Version Control). The presentation will also discuss a new open source project (Continuous Machine Learning) that has been created to adapt popular continuous integration systems like GitHub Actions and GitLab CI to data science projects.
Video producer: https://crafthub.events/