Managed MLflow on Databricks now in public preview

Posted Leave a commentPosted in Announcements, Company Blog, Data Science, Ecosystem, Engineering Blog, Machine Learning, Managed MLflow, MLflow, Platform, Product

Try this tutorial in Databricks Building production machine learning applications is challenging because there is no standard way to record experiments, ensure reproducible runs, and manage and deploy models. To address these challenges, last June we introduced MLflow, an open source platform to manage the ML lifecycle that works with any machine learning library and […]

Accelerating Machine Learning on Databricks: On-Demand Webinar and FAQ Now Available!

Posted Leave a commentPosted in Company Blog, Data Science, Databricks Runtime, Deep Learning, Ecosystem, Engineering Blog, Horovod, HorovodRunner, Keras, Machine Learning, MLflow, Platform, Product, TensorFlow

Try this notebook in Databricks On January 15th, we hosted a live webinar—Accelerating Machine Learning on Databricks—with Adam Conway, VP of Product Management, Machine Learning, at Databricks and Hossein Falaki, Software Development Engineer and Data Scientist at Databricks. In this webinar, we covered some of the latest innovations brought into the Databricks Unified Analytics Platform […]

Kicking Off 2019 with an MLflow User Survey

Posted Leave a commentPosted in Apache Spark, Ecosystem, Engineering Blog, Machine Learning, MLflow

It’s been six months since we launched MLflow, an open source platform to manage the machine learning (ML) lifecycle, and the project has been moving quickly since then. MLflow fills a role that hasn’t been served well in the open source community so far: managing the development lifecycle for ML, including tracking experiments and metrics, […]

MLflow v0.8.1 Features Faster Experiment UI and Enhanced Python Model

Posted Leave a commentPosted in Apache Spark, Data Science, Ecosystem, Engineering Blog, Machine Learning, Machine Learning Life Cycle, MLflow, Model Management, Platform, Spark UDF

Try this notebook in Databricks MLflow v0.8.1 was released this week. It introduces several UI enhancements, including faster load times for thousands of runs and improved responsiveness when navigating runs with many metrics and parameters. Additionally, it expands support for evaluating Python models as Apache Spark UDFs and automatically captures model dependencies as Conda environments. […]

MLflow v0.8.0 Features Improved Experiment UI and Deployment Tools

Posted Leave a commentPosted in Engineering Blog, Machine Learning, Machine Learning Life Cycle, MLflow, Model Management

Last week we released MLflow v0.8.0 with multiple new features, including improved UI experience and support for deploying models directly via Docker containers to theAzure Machine Learning Service Workspace. Now available on PyPI and with docs online, you can install this new release with pip install mlflow as described in the MLflow quickstart guide. In […]

MLflow v0.7.0 Features New R API by RStudio

Posted Leave a commentPosted in Announcements, Apache Spark, Company Blog, Deep Learning, Ecosystem, Education, Engineering Blog, GPyOpt, Hyperopt, Java, Keras, Machine Learning, MLflow, multistep workflow, Partners, python, R, RStudio

Today, we’re excited to announce MLflow v0.7.0, released with new features, including a new MLflow R client API contributed by RStudio. A testament to MLflow’s design goal of an open platform with adoption in the community, RStudio’s contribution extends the MLflow platform to a larger R community of data scientists who use RStudio and R […]

How to Use MLflow To Reproduce Results and Retrain Saved Keras ML Models

Posted Leave a commentPosted in Apache Spark, Engineering Blog, Keras, Machine Learning, MLflow, Model Management, Platform, TensorFlow, Unified Analytics Platform

In part 2 of our series on MLflow blogs, we demonstrated how to use MLflow to track experiment results for a Keras network model using binary classification. We classified reviews from an IMDB dataset as positive or negative. And we created one baseline model and two experiments. For each model, we tracked its respective training […]

New Features in MLflow v0.6.0

Posted Leave a commentPosted in Data Science, Engineering Blog, Machine Learning, MLflow, Model Management, Platform, Spark ML

Today, we’re excited to announce MLflow v0.6.0, released early in the week with new features. Now available on PyPI and Maven, the docs are updated. You can install the recent release with pip install mlflow as described in the MLflow quickstart guide. MLflow v0.6.0 introduces a number of major features: A Java client API, available […]

MLflow On-Demand Webinar and FAQ Now Available!

Posted Leave a commentPosted in Data Science, Deep Learning, Ecosystem, Engineering Blog, Machine Learning, MLflow, Model Management, Platform, Product, Unified Analytics Platform

On August 30th, our team hosted a live webinar—Introducing MLflow: Infrastructure for a complete Machine Learning lifecycle—with Matei Zaharia, Co-Founder and Chief Technologist at Databricks. In this webinar, we walked you through MLflow, a new open source project from Databricks that aims to design an open ML platform where organizations can use any ML library […]

How to Use MLflow to Experiment a Keras Network Model: Binary Classification for Movie Reviews

Posted Leave a commentPosted in Apache Spark, Data Science, Engineering Blog, Machine Learning, MLflow, Model Management, Platform, python, Unified Analytics Platform

In the last blog post, we demonstrated the ease with which you can get started with MLflow, an open-source platform to manage machine learning lifecycle. In particular, we illustrated a simple Keras/TensorFlow model using MLflow and PyCharm. This time we explore a binary classification Keras network model. Using MLflow’s Tracking APIs, we will track metrics—accuracy […]