Have you ever strugged with having different environments to build, train and serve ML models, and how to orchestrate between them? While DevOps and GitOps have made huge traction in recent years, many customers struggle to apply these practices to ML workloads.

This talk will focus on ways to effectively infuse AI into production-grade applications through establishing practices around model reproducibility, validation, versioning/tracking, and safe/compliant deployment. We will demonstrate how to run an E2E machine learning system using nothing more than Git. This will integrate DevOps, data and ML pipelines together, and show how to use multiple workload orchestrators together.

While the examples will be run using Azure Pipelines, Azure ML and Kubeflow, we will also show how to extend these platforms to any orchestration tool.