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
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...
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...
Simplify Your Forecasting With Databricks AutoML
Last year, we announced Databricks AutoML for Classification and Regression and showed the importance of having a glass box approach to empower data...
Scaling SHAP Calculations With PySpark and Pandas UDF
Motivation With the proliferation of applications of Machine Learning (ML) and especially Deep Learning (DL) models in decision making, it is becoming more...
Implementing MLOps on Databricks using Databricks notebooks and Azure DevOps, Part 2
This is the second part of a two-part series of blog posts that show an end-to-end MLOps framework on Databricks, which is based...
Enabling Computer Vision Applications With the Data Lakehouse
Read Rise of the Data Lakehouse to explore why lakehouses are the data architecture of the future with the father of the data...