How to Orchestrate Machine Learning Workflows with Airflow

This webinar provides an overview of machine learning orchestration, its importance in managing ML workflows, and how it fits in with MLOps. We cover everything you need to know to use Airflow to streamline your ML workflows and MLOps environments including orchestrating tasks such as data ingestion, data processing, model training, model deployment, and ongoing model monitoring. Questions covered in this session include:

• What is machine learning orchestration and how can Airflow be used for this purpose?
• What operators are available for Airflow that can be used for machine learning orchestration?
• What are some best practices for setting up and managing an Airflow environment for ML workflows?
• How can I optimize Airflow performance for ML use cases and avoid common pitfalls?

All code covered in this webinar can be found in this repo.