Articles

All the latest words and talk on AI Infrastructure

7 Rules for Bulletproof, Reproducible Machine Learning R&D

7 Rules for Bulletproof, Reproducible Machine Learning R&D

So, if you’re a nose-to-the-keyboard developer, there’s ample probability that this analogy is outside your comfort zone … bear with me.  Imagine two Olympics-level figure skaters working together on the ice, day in and day out, to develop and perfect a medal-winning...

The MLOps Stack

The MLOps Stack

What is MLOps (briefly) MLOps is a set of best practices that revolve around making machine learning in production more seamless. The purpose is to bridge the gap between experimentation and production with key principles to make machine learning reproducible,...

What is a Feature Store

What is a Feature Store

Data teams are starting to realize that operational machine learning requires solving data problems that extend far beyond the creation of data pipelines. In Tecton’s previous post, Why We Need DevOps for ML Data, we highlighted some of the key data challenges that...

12 Steps to Reproducible Machine Learning in Production

12 Steps to Reproducible Machine Learning in Production

The last two decades have yielded us some great understandings about Software Development. A big part of that is due to the emergence of DevOps and it’s wide adoption throughout the industry. Leading software companies follow identical patterns: Fast iterations in...

Building an AI Red Team – Talk at Red Hat OpenShift Commons

Building an AI Red Team – Talk at Red Hat OpenShift Commons

At my keyote for the Red Hat OpenShift Commons AI Conference I talked about building an AI Red Team whose job it is to fix  AI when it goes wrong.  With algorithms making more and more decisions in our lives, from who gets a job, to who gets hired and fired, and even...