Standardizing the Machine Learning Lifecycle

Data science simplified

Download the eBook today!

Successfully building and deploying a machine learning (ML) model is difficult to do once. Reproducing that model at scale — replicating your pipeline, comparing the results of different versions, tracking what’s running where, and redeploying and rolling back updated models — is much harder.

That’s why so many businesses in every industry are scrambling to effectively manage ML models throughout their lifecycle and apply engineering best practices.

In this updated eBook, we’ll explore what makes the ML lifecycle so challenging compared with traditional software-development. And you’ll discover why MLflow has emerged as a leader in automating the end-to-end ML lifecycle. With over 2 million downloads a month — and growing support in the developer community — this open source platform is simplifying the complex process of standardizing ML Ops and productionizing ML models.

This updated eBook explores the advantages of MLflow and introduces you to the newest component: MLflow Model Registry. You’ll also discover how MLflow fits into the open, unified Databricks Unified Data Analytics Platform for data engineering, science and analytics.

Here’s what you’ll learn:

  • Key challenges faced by organizations when managing ML models throughout their lifecycle and how to overcome them
  • How MLflow address these challenges, including experiment tracking, project reproducibility, model deployment, and model management
  • How Managed MLflow provides a fully managed, integrated experience with enterprise reliability, security and scale on the Databricks Unified Data Analytics Platform
  • NEW Introduction to MLflow’s latest feature: Model Registry

Read this eBook to learn more.