Quickly Deploy, Test, and Manage ML Models as REST Endpoints with MLflow Model Serving on Databricks
MLflow Model Registry now provides turnkey model serving for dashboarding and real-time inference, including code snippets for tests, controls, and automation. MLflow Model Serving on Databricks provides a turnkey solution to host machine learning (ML) models as REST endpoints that are updated automatically, enabling data teams to own the end-to-end lifecycle of a real-time machine...
Announcing MLflow Model Serving on Databricks
Databricks MLflow Model Serving provides a turnkey solution to host machine learning (ML) models as REST endpoints that are updated automatically, enabling data science teams to own the end-to-end lifecycle of a real-time machine learning model from training to production. When it comes to deploying ML models, data scientists have to make a choice based...
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...
