Dagster Deep Dive Recap: Orchestrating Flexible Compute for ML with Dagster and Modal

Dagster and Modal are used together to automate and streamline machine learning workflows, providing scalable and flexible infrastructure for heavy computing tasks like model training and data processing. Dagster's orchestration features help manage complex configurations, while Modal offers scalability without complexity, allowing teams to focus on development. The tools can be combined to automate podcast summarization, demonstrating their strength in handling parallelism and large-scale data processing. By using Dagster and Modal, teams can simplify pipeline orchestration, reduce infrastructure complexity, and focus on building applications instead of managing complex infrastructure.