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:
Read this eBook to learn more.