Explore Continuous Integration and Continuous Deployment (CI/CD) for Machine Learning in this 58-minute webinar from Open Data Science. Learn how to automate ML model training and evaluation using CML (Continuous Machine Learning) and best practices from software engineering. Discover techniques for automatically allocating and shutting down cloud instances, generating performance reports in pull/merge requests, transferring data between cloud storage and computing instances, and customizing automation workflows with GitLab CI/CD. Gain insights into common problems, assumptions, and practical implementations of CI/CD for ML projects. Cover topics such as two-step workflows, neural style transfer, GPU usage, automation importance, handling streaming data, running notebooks in the cloud, and comparisons with other ML tools like MLflow and DVC. By the end of this webinar, acquire the knowledge to streamline your ML development process and improve collaboration within data science teams.