The Latest In Machine Learning, Enterprise AI, and MLOps
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.
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.
We’re proud to announce the new TWIML Solutions Guide, a directory of machine learning tools and platform technologies for data scientists, ML engineers and other AI practitioners and leaders. The Guide aims to help them explore and compare open source and commercial offerings for building, delivering, and improving their ML and AI projects. This post explains why we think the guide is important and highlights some of its key features.
Before we can discuss enabling and scaling the delivery of machine learning, we must understand the full scope of the machine learning process. While much of the industry dialogue around machine learning continues to be focused on modeling, the reality is that modeling is a small part of the process.