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 […]

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 […]

New Features in MLflow v0.5.0 Release

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

Today, we’re excited to announce MLflow v0.5.0, which we released last week with some new features. MLflow 0.5.0 is already available on PyPI and docs are updated. If you do pip install mlflow as described in the MLflow quickstart guide, you will get the recent release. In this post, we’ll describe new features and fixes […]

MLflow 0.4.2 Released – The Databricks Blog

Posted Leave a commentPosted in Announcements, Apache Spark, Company Blog, Engineering Blog, Machine Learning, MLflow, Model Management

Today, we’re excited to announce MLflow v0.4.0, MLflow v0.4.1, and v0.4.2 which we released within the last week with some of the recently requested features. MLflow 0.4.2 is already available on PyPI and docs are updated. If you do pip install mlflow as described in the MLflow quickstart guide, you will get the recent release. In this […]

Bay Area Apache Spark Meetup Summary @ Databricks HQ

Posted Leave a commentPosted in Apache Spark, Company Blog, Deep Learning, Events, Machine Learning, MLflow, Model Management, python, TensorFlow

On July 19, we held our monthly Bay Area Spark Meetup (BASM) at Databricks, HQ in San Francisco. At the Spark + AI Summit in June, we announced two open-source projects: Project Hydrogen and MLflow. Partly to continue sharing the progress of these open-source projects with the community and partly to encourage community contributions, two […]

MLflow v0.3.0 Released – The Databricks Blog

Posted Leave a commentPosted in Announcements, Apache Spark, Company Blog, Engineering Blog, Machine Learning, MLflow, Model Management

Today, we’re excited to announce MLflow v0.3.0, which we released last week with some of the requested features from internal clients and open source users. MLflow 0.3.0 is already available on PyPI and docs are updated. If you do pip install mlflow as described in the MLflow quickstart guide, you will get the recent release. […]