Adapting Continuous Integration and Continuous Delivery for Machine Learning

Explore the challenges and solutions of implementing continuous integration and continuous delivery (CI/CD) in machine learning projects through this insightful conference talk by Elle O'Brien, Data Scientist at Iterative.ai. Discover how the emerging field of MLOps adapts DevOps practices to balance rapid experimentation with production stability in ML projects. Learn about open-source tools like Git, GitHub Actions, and DVC (Data Version Control) that enable teams to automate model training and evaluation, track experiments, and streamline the model selection process. Gain valuable insights into creating efficient workflows for heterogeneous teams of ML engineers, data scientists, and software engineers, addressing the complexities of managing experiments, changing datasets, and full-stack project demands in the maturing discipline of machine learning.