Databricks Announces the First Feature Store Co-designed with a Data and MLOps Platform
Today, we announced the launch of the Databricks Feature Store, the first of its kind that has been co-designed with Delta Lake and MLflow to accelerate ML deployments. It inherits all of the benefits from Delta Lake, most importantly: data stored in an open format, built-in versioning and automated lineage tracking to facilitate feature discovery....
MLflow Model Registry on Databricks Simplifies MLOps With CI/CD Features
MLflow helps organizations manage the ML lifecycle through the ability to track experiment metrics, parameters, and artifacts, as well as deploy models to batch or real-time serving systems. The MLflow Model Registry provides a central repository to manage the model deployment lifecycle, acting as the hub between experimentation and deployment. A critical part of MLOps,...
Databricks Extends MLflow Model Registry with Enterprise Features
We are excited to announce new enterprise grade features for the MLflow Model Registry on Databricks. The Model Registry is now enabled by default for all customers using Databricks' Unified Analytics Platform. In this blog, we want to highlight the benefits of the Model Registry as a centralized hub for model management, how data teams...
MLflow 0.2 Released
At this year’s Spark+AI Summit, we introduced MLflow, an open source platform to simplify the machine learning lifecycle. In the 3 weeks since the release, we’ve already seen a lot of interest from data scientists and engineers in using and contributing to MLflow. MLFlow’s GitHub repository already has 180 forks, and over a dozen contributors...
