Collaborating Authors

How to Share and Control ML Model Access with MLflow Model Registry


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 across organizations can share and control access to their models, and touch upon how you can use Model Registry APIs for integration or inspection. MLflow already has the ability to track metrics, parameters, and artifacts as part of experiments; package models and reproducible ML projects; and deploy models to batch or real-time serving platforms. Built on these existing capabilities, the MLflow Model Registry [AWS] [Azure] provides a central repository to manage the model deployment lifecycle.

Managed MLflow Now Available on Databricks Community Edition - The Databricks Blog


In February 2016, we introduced Databricks Community Edition, a free edition for big data developers to learn and get started quickly with Apache Spark. Since then our commitment to foster a community of developers remains steadfast: to date, we have over 150K registered Community Edition users; we have trained thousands of people at meetups and Spark AI Summits, and other open-source events. Today, we are excited to extend Databricks Community Edition with hosted MLflow for free, as part of our ongoing commitment to help developers learn about machine learning lifecycle. With the Community Edition, you can try tutorials that demonstrate how to track results and experiments as you build machine learning models--a crucial stage in the machine learning model's development lifecycle. MLflow is an open-source platform for the machine learning lifecycle with four components: MLflow Tracking, MLflow Projects, MLflow Models, and MLflow Registry.

How Databricks' MLflow Model Registry 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, or ML lifecycle management, is continuous integration and deployment (CI/CD). In this post, we introduce new features in the Model Registry on Databricks [AWS] [Azure] to facilitate the CI/CD process, including tags and comments which are now enabled for all customers, and the upcoming webhooks feature currently in private preview. Today at the Data AI Summit, we announced the general availability of Managed MLflow Model Registry on Databricks, and showcased the new features in this post.

PyCaret 2.1 is here: What's new? - KDnuggets


We are excited to announce PyCaret 2.1 -- update for the month of Aug 2020. PyCaret is an open-source, low-code machine learning library in Python that automates the machine learning workflow. It is an end-to-end machine learning and model management tool that speeds up the machine learning experiment cycle and makes you 10x more productive. In comparison with the other open-source machine learning libraries, PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with few words only. This makes experiments exponentially fast and efficient.