Architecting MLOps on the Lakehouse
Here at Databricks, we have helped thousands of customers put Machine Learning (ML) into production. Shell has over 160 active AI projects saving…
Data Science and ML
Build Reliable Production Data and ML Pipelines With Git Support for Databricks Workflows
We are happy to announce native support for Git in Databricks Workflows, which enables our customers to build reliable production data and ML…
Introduction to Analyzing Crypto Data Using Databricks
The market capitalization of cryptocurrencies increased from $17 billion in 2017 to $2.25 trillion in 2021. That’s over a 13,000% ROI in a…
Announcing General Availability of Databricks Feature Store
Today, we are thrilled to announce that Databricks Feature Store is generally available (GA)! In this blog post, we explore how Databricks Feature…
How Wrong Is Your Model?
In this blog, we look at the topic of uncertainty quantification for machine learning and deep learning. By no means is this a…
Simplifying Change Data Capture With Databricks Delta Live Tables
This guide will demonstrate how you can leverage Change Data Capture in Delta Live Tables pipelines to identify new records and capture changes…
Model Evaluation in MLflow
Many data scientists and ML engineers today use MLflow to manage their models. MLflow is an open-source platform that enables users to govern…
Supercharge Your Machine Learning Projects With Databricks AutoML — Now Generally Available!
Machine Learning (ML) is at the heart of innovation across industries, creating new opportunities to add value and reduce cost. At the same…
Building a Geospatial Lakehouse, Part 2
In Part 1 of this two-part series on how to build a Geospatial Lakehouse, we introduced a reference architecture and design principles to…
Cross-version Testing in MLflow
MLflow is an open source platform that was developed to manage the ML lifecycle, including experimentation, reproducibility, deployment, and a central model registry.…
