MLflow v0.9.0 Features SQL Backend, Projects in Docker, and Customization in Python Models

Posted Leave a commentPosted in Apache Spark, docker, Engineering Blog, Machine Learning, Machine Learning Life Cycle, MLflow, Platform, python, SQLAlchemy

MLflow v0.9.0 was released this week. It introduces a set of new features and community contributions, including SQL store for tracking server, support for MLflow projects in Docker containers, and simple customization in Python models. Additionally, this release adds a plugin scheme to customize MLflow backend store for tracking and artifacts. Now available on PyPI […]

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 […]