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…
Data Science and ML
Enabling Computer Vision Applications With the Data Lakehouse
The potential for computer vision applications to transform retail and manufacturing operations, as explored in the blog Tackle Unseen Quality, Operations and Safety…
Are GPUs Really Expensive? Benchmarking GPUs for Inference on Databricks Clusters
It is no secret that GPUs are critical for artificial intelligence and deep learning applications since their highly-efficient architectures make them ideal for…
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 thank…
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…
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…
