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 Superb AI | Nov 11, 2020 | AutoML, Infrastructure, Labeling, MLOps
In this post we’ll dive into the machine learning theory and techniques that were developed to evaluate our auto-labeling AI at Superb AI. More specifically, how our data platform estimates the uncertainty of auto-labeled annotations and applies it to active...
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...
by AI Infrastructure Alliance | Mar 12, 2020 | AutoML
Machine Learning at scale is a complex web of glued together software. Building an enterprise pipeline that’s seamless and easy to use is a challenge most companies never seem to get right. The tools that support AI seem like they’re almost a decade...
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