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...
The Delta Between ML Today and Efficient ML Tomorrow
Delta Lake and MLflow both come up frequently in conversation but often as two entirely separate products. This blog will focus on the...
Monitoring ML Models With Model Assertions
This is a guest post from the Stanford University Computer Science Department. We thank Daniel Kang, Deepti Raghavan and Peter Bailis of Stanford...
AML Solutions at Scale Using Databricks Lakehouse Platform
Anti-Money Laundering (AML) compliance has been undoubtedly one of the top agenda items for regulators providing oversight of financial institutions across the globe...
Feature Engineering at Scale
Feature engineering is one of the most important and time-consuming steps of the machine learning process. Data scientists and analysts often find themselves...
Using Bayesian Hierarchical Models to Infer the Disease Parameters of COVID-19
In a previous post, we looked at how to use PyMC3 to model the disease dynamics of COVID-19 . This post builds on...
Three Principles for Selecting Machine Learning Platforms
This blog post is the second in a series on ML platforms, operations, and governance. For the first post, see Rafi Kurlansik’s post...