by Daniel Jeffries | Jan 7, 2022 | AI Hardware, AutoML, Data Lineage, Data Versioning, Feature Store, Governance, Hyperparameter Optimization, Infrastructure, Labeling, Logging, MLOps, MLOPs Landscape, Monitoring, Production
Just a few years ago, almost nobody was building software to support the surge of new machine learning apps coming into production all over the world. Every cutting-edge tech company, like Google, Lyft, Microsoft, and Amazon rolled their own AI/ML tech stack from...
by Daniel Jeffries | Dec 4, 2020 | Data Lineage, Data Versioning, Pachyderm
What is data lineage? At its most basic it’s the history of your data. It tells us where that data comes from, where it lives and how it’s transformed over time. As AI teams pour over data looking for patterns, or process it and massage it to get it into shape for...
by Daniel Jeffries | Oct 16, 2020 | Infrastructure, MLOps, Open Source
With every generation of computing comes a dominant new software or hardware stack that sweeps away the competition and catapults a fledgling technology into the mainstream. I call it the Canonical Stack (CS). Think the WinTel dynasty in the 80s and 90s, with...
by Daniel Jeffries | Sep 10, 2020 | MLOps
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...
by Daniel Jeffries | Jul 10, 2020 | AutoML, Infrastructure
In this article, KD Nuggets authors Joseph Chin, Aifaz Gowani, Gabriel James, and Matthew Peng ask if AutoML services from Amazon, Google and Microsoft will replace data scientists in the long run. The data science pipeline is a complicated one with a lot of manual...
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