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Machine Learning models tend to perform inconsistently across different parts of a dataset. Summary performance metrics such as AUC, and F1, are not enough to identify the parts of the data where a model need...

Hugging Face provides awesome APIs for Natural Language Modeling. In particular, they make working with large transformer models incredibly easy. These models can be used off-the-shelf for text generation, translation, an...

Comet recently hosted the online panel, “How do top AI researchers from Google, Stanford and Hugging Face approach new M...

Comet recently hosted the online panel, “How do top AI researchers from Google, Stanford and Hugging Face approach new ML problems?” This is the second post in a series where we recap the q...

Comet recently hosted the online panel, “How do top AI researchers from Google, Stanford and Hugging Face approach new ...

Introduction 3D Histograms or Ridge Plots are a great way to visualize the training progress of your Neural Network. Histogram distributions of the weights, gradients, and activations allow us to g...

In Machine Learning, a "model" could be anything. The following pasta machine could be described as a model. It takes "input", and a set of "hyperparameters". Well, at least a couple of hyperparameters: the "cutter" d...

Introduction A confusion matrix is a visual way to inspect the performance of a classification model. Metrics such as accuracy can be inadequate in cases where there are large class imbalances in the data, a p...

Intro by Niko Laskaris We recently released a code-based custom visualization builder called Custom Panels. As part of the rollout, we’re featuring user stories from some of the awesome Rese...