Databricks first introduced MLflow in last June. Immediately, startups and larger enterprises started using it to manage their machine learning lifecycles. Since then, more than 80 contributors from some 40 companies have worked on the open source machine learning tool, and it regularly sees more than 500,000 downloads per month.

And check out this recent news:

Unveiled at the Spark + AI Summit 2019, sponsored by Databricks, the new Databricks and Microsoft collaboration is a sign of the companies’ deepening ties, but it is also too new to say how effectively the partnership will advance MLflow for developers, said Mike Gualtieri, a Forrester analyst.

In this talk, Andrei Varanoch demonstrates the blueprint for such a Lambda Architecture implementation in Microsoft Azure, with Azure Databricks — a PaaS Spark offering – as a key component.  The term “Lambda Architecture” stands for a generic, scalable and fault-tolerant data processing architecture. As the hyper-scale now offers a various PaaS services for data ingestion, storage and processing, the need for a revised, cloud-native implementation of the lambda architecture is arising.