Building resilient machine learning pipelines is crucial for scaling AI solutions. This talk will outline the best practices for structuring ML workflows in production environments, covering Python best practices, DAG-based pipeline design, and effective packaging strategies. Manasa Hari will explore approaches for testing and deploying ML systems, ensuring seamless integration with platform workflows. Designed for platform engineers and data scientists, this session will provide practical insights into streamlining the transition from development to deployment, enabling the creation of robust and scalable AI solutions.