MLflow v0.8.1 Features Faster Experiment UI and Enhanced Python Model
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. Now available on PyPI and...
MLflow v0.8.0 Features Improved Experiment UI and Deployment Tools
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 the Azure 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....
MLflow v0.7.0 Features New R API by 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...
New Features in MLflow v0.6.0
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...
New Features in MLflow v0.5.2 Release
Today, we’re excited to announce MLflow v0.5.0, MLflow v0.5.1, and MLflow v0.5.2, which were released last week with some new features. MLflow 0.5.2 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 post, we’ll...
MLflow 0.4.2 Released
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...
MLflow v0.3.0 Released
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....
Cloud-based Relational Database Management Systems at Databricks
Databricks and Microsoft have jointly developed a new cloud service called Microsoft Azure Databricks, which makes Apache Spark analytics fast, easy, and collaborative on the Azure cloud. Not only does this new service allow data scientists and data engineers to be more productive and work collaboratively with their respective teams, but it also gives them...
Declarative Infrastructure with the Jsonnet Templating Language
This blog post is part of our series of internal engineering blogs on Databricks platform, infrastructure management, integration, tooling, monitoring, and provisioning. At Databricks engineering, we are avid fans of Kubernetes. Much of our platform infrastructure runs within Kubernetes, whether in AWS cloud or more regulated environments. However, we have found that Kubernetes alone is...
Next Generation Physical Planning in Apache Spark
Never underestimate the bandwidth of a station wagon full of tapes hurtling down the highway. — Andrew Tanenbaum, 1981 magine a cold, windy day at Lake Tahoe in California. When a group of Bricksters who were cross-country skiing stopped for lunch, one found a 4TB hard drive in their backpack. One brickster remarked that this...
