Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycle for On-Prem or in the Cloud

Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycle for On-Prem or in the Cloud

Wednesday, May 22
11:50 AM - 12:30 PM
Marquis Salon 7

Specialized tools for machine learning development and model governance are becoming essential. MlFlow is an open source platform for managing the machine learning lifecycle. Just by adding a few lines of code in the function or script that trains their model, data scientists can log parameters, metrics, artifacts (plots, miscellaneous files, etc.) and a deployable packaging of the ML model. Every time that function or script is run, the results will be logged automatically as a byproduct of those lines of code being added, even if the party doing the training run makes no special effort to record the results. MLflow application programming interfaces (APIs) are available for the Python, R and Java programming languages, and MLflow sports a language-agnostic REST API as well. Over a relatively short time period, MLflow has garnered more than 3,300 stars on GitHub , almost 500,000 monthly downloads and 80 contributors from more than 40 companies. Most significantly, more than 200 companies are now using MLflow. We will demo MlFlow Tracking , Project and Model components with Azure Machine Learning (AML) Services and show you how easy it is to get started with MlFlow on-prem or in the cloud.

Presentation Video

SPEAKERS

alex zeltov
Global Black Belt TSP - Big Data and Advanced Analytics
microsoft
Alex Zeltov is a Global Blackbelt TSP in Big Data and Advanced Analytics at Microsoft with over 17 years of industry experience in Information Technology and most recently in Big Data and Predictive Analytics. Prior to joining Microsoft Alex worked as a Big Data Solutions Architect at Hortonworks.