Explore the automation of machine learning workflows in this 50-minute webinar. Learn how to streamline the process of data gathering, model training, deployment, and testing into a single, executable command. Discover techniques for containerizing various stages of the ML pipeline, including data preparation, model training, and deployment to Kubernetes clusters. Gain insights into production inferencing, model performance monitoring, and the use of Hydrosphere.io for managing ML workflows. Understand how to parameterize functions, define containers for different pipeline stages, and compile them into a cohesive workflow. Delve into topics such as mounting volumes, uploading models, and implementing testing and cleaning procedures. By the end of this webinar, you'll be equipped to create a more efficient and continuous delivery process for your machine learning projects.