How to Display Model Metrics in Dashboards using the MLflow Search API
Machine learning engineers and data scientists frequently train models to optimize a loss function. With optimization methods like gradient descent, we iteratively improve upon our loss, eventually arriving at a minimum. Have you ever thought: Can I optimize my own productivity as a data scientist? Or can I visually see the progress of my training...
Analyzing Your MLflow Data with DataFrames
Max Allen interned with Databricks Engineering in the Summer of 2019. This blog post, written by Max, highlights the great work he did while on the team. Introduction to MLflow and the Machine Learning Development Lifecycle MLflow is an open source platform for the machine learning lifecycle, and many Databricks customers have been using it...
Announcing the MLflow 1.1 Release
We’re excited to announce today the release of MLflow 1.1. In this release, we’ve focused on fleshing out the tracking component of MLflow and improving visualization components in the UI. Some of the major features include: Automatic logging from TensorFlow and Keras Parallel coordinate plots in the tracking UI Pandas DataFrame based search API Java...