How to Orchestrate Machine Learning Workflows with Airflow

Summary

Although often regarded as a data engineering tool, Airflow is also wildly popular among machine learning (ML) teams. In this webinar on machine learning orchestration with Apache Airflow, we’ll provide an overview of machine learning orchestration, its importance in managing ML workflows, and how it fits in with MLOps. We’ll delve into how Airflow can be used for machine learning orchestration and MLOps, including orchestrating tasks such as data ingestion, data processing, model training, model deployment and ongoing model monitoring.

Specifically, we’ll discuss:

• The operators available for Airflow that can be used for machine learning orchestration.
• Best practices for setting up and managing an Airflow environment for ML workflows, including DAG design, infrastructure management, CI/CD integration, and version control.
• How to optimize Airflow performance for ML use cases and avoid common pitfalls.

By the end of this webinar, you’ll have a comprehensive understanding of machine learning orchestration with Airflow and how it can help streamline your ML workflows and MLOps environments.