Successfully building and deploying a machine-learning model can be difficult to do once. Enabling other data scientists (or yourself) to reproduce your pipeline, compare the results of different versions, track what’s running where, and redeploy and rollback updated models, is much harder.
We’ll explore in greater depth what makes the ML lifecycle so challenging compared to the traditional software-development lifecycle, and share the Databricks approach to addressing these challenges.
We’ll cover:
- Key challenges faced by organizations when managing ML models throughout their lifecycle and how to overcome them.
- How MLflow, an open source framework unveiled by Databricks, can help address these challenges, specifically around experiment tracking, project reproducibility, and model deployment.
- How Managed MLflow on Databricks provides a fully managed, integrated experience with enterprise reliability, security, and scale on the Databricks Unified Analytics Platform.
Read this eBook to learn more.