Explore a 35-minute talk on productionizing machine learning models using microservices architecture. Learn how to streamline the process of moving workloads from training to production by running Spark as a microservice for inferencing. Discover techniques for achieving auto-scaling, versioning, and security in machine learning deployments. Gain insights into feeding feature vectors aggregated from multivariate real-time and historical data to machine learning models and serverless functions for real-time dashboards and actions. Delve into topics such as machine learning pipelines, service composition, workflow automation, microservices architecture, monitoring, and running Spark in distributed clusters. Understand how to create larger pipelines, define domain-specific languages, and implement automated workflows for efficient machine learning operations.