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It's been 2 years since we originally launched MLflow, an open source platform for the full machine learning lifecycle, and we are thrilled and humbled by the adoption and impact it has gained in the data science and data engineering community. With now over 2M+ monthly downloads, 200 code contributors and over a 100 contributing organizations, MLflow is the fastest growing and most widely used open source machine learning platform, confirming the need for an open source approach to help manage the complete ML lifecycle.

MLFlow-focused talks, trainings, and tutorials<br />
 <br />
featured at the Spark +AI Virtual Summit 2020.
We provided an overview of how MLflow helps manage the ML lifecycle with a representation of a diverse set of use cases and industries during a recent virtual conference focused on ML platforms, and we have much more coming at Spark + AI Summit. Below is a list of sessions, tutorials, and trainings on MLflow for you to dive in.


Join Matei Zaharia on Thursday, June 25th for his keynote on Simplifying Model Development and Management with MLflow to learn more about some of the most recent and new MLflow features. Specifically, he will cover what's new in MLflow to further streamline the ML lifecycle with simplified experiment tracking, model management, and model deployment with the new MLflow Model Registry. Many organizations face challenges tracking which models are available in the organization and which ones are in production. The MLflow Model Registry provides a centralized database to keep track of these models, share and describe new model versions, and deploy the latest version of a model through APIs.


We have a fantastic lineup of speakers and sessions throughout the conference on MLflow. Join experts from Accenture, ExxonMobil, Zynga, Atlassian, Databricks and more for real-life examples and deep dives on MLflow (in chronological order):

Free Tutorial

Last but not least, you can join Using MLflow for end-to-end machine learning on Databricks for a free 80-minute tutorial presented by Sean Owen of Databricks. In this session, we'll take a look at a simple example where health data can be used to predict life expectancy. It will start with data engineering in Apache SparkTM, data exploration, model tuning and logging with hyperopt and MLflow. It will continue with examples of how the model registry governs model promotion, and simple deployment to production with MLflow as a job or dashboard.

Next Steps

You can browse through our sessions from the Spark +AI 2020 Summit schedule, too.

To get started with open source MLflow, follow the instructions at or check out the release code on Github. We are excited to hear your feedback!

If you’re an existing Databricks user, you can start using managed MLflow on Databricks by importing the Quick Start Notebook for Azure Databricks or AWS. If you’re not yet a Databricks user, visit to learn more and start a free trial of Databricks and managed MLflow.

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