A Guide to MLflow Talks at Data + AI Summit Europe 2020

In the last two years since its release, MLflow has seen a rapid adoption among enterprises and the data science community. With over 2M downloads, 260 contributors, and 100+ organizations contributing, the momentum seems to grow each year.

We have put together a short list of picks–keynotes, tutorials, and sessions–for MLflow: how the community and organizations manage their models at scale using MLflow and MLOps best practices.

earn more about the expansive list of talks, tutorials, training and other MLflow-focused programs featured at the Data + AI Virtual Summit Europe 2020.

Keynotes

Join Matei Zaharia on Thursday, November 19th for his keynote on Taking Machine Learning to Production with New Features in MLflow to learn more about some of the most recent and new MLflow features. Specifically, Matei will present some of the latest functionality added for productionizing machine learning in MLflow, the popular open source machine learning platform started by Databricks in 2018. These include built-in support for model management and review using the Model Registry, APIs for automatic Continuous Integration and Delivery (CI/CD), model schemas to catch differences in a model’s expected data format, and integration with model explainability tools.

Lin Qiao, engineering director, PyTorch, Facebook, will talk about recent developments in PyTorch and its extended integration with MLflow.

Talks

We have a fantastic lineup of speakers and sessions throughout the conference on MLflow. Join experts from H&M, Facebook, Yotpo, Seldon, Avast, Criteo, and Databricks and more for real-life examples, use cases, and deep dives on MLflow:

Die nächsten Schritte

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

To get started with open source MLflow, follow the instructions at mlflow.org or check out the MLflow 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 https://databricks.com/product/managed-mlflow to learn more and start a free trial of Databricks and managed MLflow.

DATABRICKS KOSTENLOS TESTEN Erste Schritte

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