Comet Resources
Standardizing the Experiment #2: Baseline Models (Topic Model)
As part of the Standardizing the Experiment Series, we present a report on a baseline Topic Model for the HackerNews Dataset.
Standardizing the Experiment #2: Baseline Models (Sentiment Analysis)
As part of the Standardizing the Experiment Series, we present a report on a baseline Sentiment Analysis Model for the HackerNews Dataset.
Comet Tips and Tricks
Here's a collection of tips and tricks in the Comet MLOps platform, including adding multiple metrics to a built-in chart panel, filtering experiments, setting experiments as baselines in a new project, and more.
Advanced ML: Multi-file Jobs
As the complexity of your training pipeline grows, you may find it beneficial to start modularizing code. In this report, we outline best practices for passing the Comet Experiment object between different files within a project.
Advanced ML: Parameter Optimization
A guide to using an Iterative Strategy for Hyperparameter Optimization.
Profiling Datasets
In this report we explore how to log profiles of your Pandas DataFrames, use them to assess the quality of your inputs, and identify the data constraints of your model.
Advanced ML: Parallelism
In this report we explore two of the most common methods for parallelized training in Machine Learning: Data Parallelism and Inter-Model Parallelism.
Logging Curves to Comet
Learn how to visualize your ROC and Precision-Recall Curves using Comet Reports and Panels.