by Allegro AI | Nov 30, 2020 | Hyperparameter Optimization, MLOps
The design and training of neural networks are still challenging and unpredictable procedures. The difficulty of tuning these models makes training and reproducing more of an art than a science, based on the researcher’s knowledge and experience. One of the reasons...
by Fiddler AI | Nov 20, 2020 | Infrastructure, MLOps, Monitoring
We’re living in unprecedented times wherein a matter of a few weeks, things changed dramatically for many humans and businesses across the globe. With COVID-19 spreading its wings across the globe and taking human lives we are seeing record jumps in unemployment and...
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 | 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 Valoh AI | Oct 12, 2020 | Infrastructure, MLOps
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,...
by Tecton | Oct 1, 2020 | Feature Store, MLOps
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...
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