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Last year, Databricks launched MLflow, an open source framework to manage the machine learning lifecycle that works with any ML library to simplify ML engineering. MLflow provides tools for experiment tracking, reproducible runs and model management that make machine learning applications easier to develop and deploy. In the past year, the MLflow community has grown quickly: 80 contributors from over 40 companies have contributed code to the project, and over 200 companies are using MLflow. In this talk, we’ll present our development plans for MLflow 1.0, the next release of MLflow, which will stabilize the MLflow APIs and introduce multiple new features to simplify the ML lifecycle. We’ll also discuss additional MLflow components that Databricks and other companies are working on for the rest of 2019, such as improved tools for model management, multi-step pipelines and online monitoring.

About: Databricks provides a unified data analytics platform, powered by Apache Spark™, that accelerates innovation by unifying data science, engineering and business.
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Shadi Saleh says:

Very nice, I would really appreciate if you publish the demo.

Rushy Nova says:

Is the example ML pipeline you demonstrated in this video available on GitHub?

R Kenne says:

That is what I will call a polished dev experience, typical Databricks. AWESOME

Cheng Yang says:

demo is impressive! is the demo available?

Tushar Kale says:

MLFlow is very promising. A new through breed from Databricks family. I would like to get details from Comcast use case if possible. Thank you

Nina Snyder says:

I like cats and IPOs too!

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