The Latest In Machine Learning, Enterprise AI, and MLOps

Featured Articles

re:Invent Roundup 2021
Pachyderm Profile
Building Responsible AI
Introducing TWIML’s New ML and AI Solutions Guide
Developing your Machine Learning Platform Strategy
Machine Learning Platform Case Studies
The Challenges of Doing Machine Learning at Scale
What is the Machine Learning Process?
What is Machine Learning?
The Rise of the Model Driven Enterprise

Recent Articles

For the last decade, as deep learning has become prominent, practitioners have been focused on accumulating as much data as possible, labeling it, preparing it for use, and then iterating on model architectures and hyperparameters in order to achieve our desired performance levels.
Long before starting the TWIML podcast, I worked at the intersection of the two technology shifts that ultimately enabled modern artificial intelligence: cloud computing and big data. AWS was the clear leader in cloud even back then, so I jumped at the opportunity to attend the company’s first re:Invent conference way back in 2012.
Pachyderm provides the ability to modularize, orchestrate, and scale the steps of your ML pipeline within a language-agnostic platform — with the added ability to trace the lineage and versioning of both code and data.
A recent tweet from Soft Linden illustrated the importance of strong responsible AI, governance and testing frameworks for organizations deploying public-facing machine learning applications. Following a search for "had a seizure now what", the tweet showed that Google’s "featured snippet" highlighted actions that a University of Utah healthcare site explicitly advised readers NOT to take.