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 managed MLflow…
Data Science and ML
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…
Solution Accelerator: Multi-touch Attribution
Behind the growth of every consumer-facing product is the acquisition and retention of an engaged user base. When it comes to customer acquisition,…
How We Built Databricks on Google Kubernetes Engine (GKE)
Our release of Databricks on Google Cloud Platform (GCP) was a major milestone toward a unified data, analytics and AI platform that is…
An Experimentation Pipeline for Extracting Topics From Text Data Using PySpark
This post is part of a series of posts on topic modeling. Topic modeling is the process of extracting topics from a set…
