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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...

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...

Comet Panel Contest Help us build the largest visualization gallery for machine learning! Join thousands of data scientists around the world and submit your panel. The first 200 users to submit a Panel win a free t-shirt (or m...

In the last three years since Comet was founded, our users and customers trained millions of models on anything from self-driving cars to speech recognition, and from Covid-19 protein prediction ...

Introduction Model training is arguably the most time consuming, and computationally demanding part of the Machine Learning pipeline. Depending on the complexity of your model, or search space, it ...

  Hosted by: Comet.ml and Pearl Cohen This week, we hosted a webinar with the patent experts at Pearl Cohen. During the webinar, Pearl Cohen talks about: Challenges to be aware of when...