Moneyball 2.0: Real-time Decision Making With MLB’s Statcast Data
The Oakland Athletics baseball team in 2002 used data analysis and quantitative modeling to identify undervalued players and create a competitive lineup on...
GPU-accelerated Sentiment Analysis Using Pytorch and Huggingface on Databricks
Sentiment analysis is commonly used to analyze the sentiment present within a body of text, which could range from a review, an email...
Introducing Apache Spark™ 3.2
We are excited to announce the availability of Apache Spark™ 3.2 on Databricks as part of Databricks Runtime 10.0 . We want to...
MLflow for Bayesian Experiment Tracking
This post is the third in a series on Bayesian inference ( [1] , [2] ). Here we will illustrate how to use...
Pandas API on Upcoming Apache Spark™ 3.2
We're thrilled to announce that the pandas API will be part of the upcoming Apache Spark™ 3.2 release. pandas is a powerful, flexible...
Catalog and Discover Your Databricks Notebooks Faster
This is a collaborative post from Databricks and Elsevier. We thank Darin McBeath, Director Disruptive Technologies -- Elsevier, for his contributions. As a...
Managing Model Ensembles With MLflow
In machine learning, an ensemble is a collection of diverse models that provide more predictive power together than any single model would on...
Real-time Point-of-Sale Analytics With a Data Lakehouse
Disruptions in the supply chain – from reduced product supply and diminished warehouse capacity – coupled with rapidly shifting consumer expectations for seamless...
Announcing Databricks Autologging for Automated ML Experiment Tracking
Machine learning teams require the ability to reproduce and explain their results--whether for regulatory, debugging or other purposes. This means every production model...
Improving On-Shelf Availability for Items With AI Out of Stock Modeling
This post was written in collaboration with Databricks partner Tredence. We thank Rich Williams, Vice President Data Engineering, and Morgan Seybert, Chief Business...