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Accelerate your Hyperparameter Optimization with PyTorch’s Ecosystem Tools

Accelerate your Hyperparameter Optimization with PyTorch’s Ecosystem Tools

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...
Explainable Monitoring: Stop Flying Blind and Monitor Your AI – Fiddler Team

Explainable Monitoring: Stop Flying Blind and Monitor Your AI – Fiddler Team

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...
7 Rules for Bulletproof, Reproducible Machine Learning R&D

7 Rules for Bulletproof, Reproducible Machine Learning R&D

by Allegro AI | Nov 13, 2020 | Infrastructure

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...
How to Improve Data Labeling Efficiency with Auto-Labeling, Uncertainty Estimates, and Active Learning

How to Improve Data Labeling Efficiency with Auto-Labeling, Uncertainty Estimates, and Active Learning

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...

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  • Feature Engineering for Recommendation Systems – Part 1

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