Comet’s machine learning platform is trusted by thousands of ML practitioners and 450 enterprise, startup, and academic teams. They use it to track, compare, explain, and optimize their models across the complete ML lifecycle – from experiment management to monitoring models in production. Here are some of their stories.
Unlocking Real-time Predictions with Shopify's Machine Learning Platform
Shopify shares how they used Comet’s Model Registry and Experiment tracking tool, Merlin's online inference, Merlin's pipelines and Pano Feature store to enhance the Merlin platform and build a robust solution to serve machine learning models for real-time predictions across multiple teams and use-cases.
How CareRev Uses Comet MPM and Experiment Management to Accelerate Innovation
Machine learning is a highly iterative process that requires the diligent tracking of multiple sources of variability. Tracking changing variables becomes extremely difficult when dealing with multiple experiments simultaneously. An experiment management tool like Comet makes model development more repeatable and transparent by organizing your entire workflow from start to finish.
Scaling ML Operations for a Multi-Sided Retail Marketplace: How Shipt Leverages Comet
Shipt, a well-known company that connects personal shopping and delivery through technology, uses Comet to help efficiently scale their machine learning (ML) operations for their multi-sided retail marketplace. Comet helps Shipt adopt a hybrid platform approach and effectively manage their non-monolithic ML platform. By using Comet's Model Registry to log trained models, Shipt can focus on building certain tools in-house while relying on Comet for specialized features. This allows them to prioritize their resources and streamline processes
Building an end-to-end Speech Recognition model in PyTorch with AssemblyAI
Deep Learning has changed the game in speech recognition with the introduction of end-to-end models. These models take in audio, and directly output transcriptions. Two of the most popular end-to-end models today are Deep Speech by Baidu, and Listen Attend Spell (LAS) by Google.
How Scientists at Uber Use Comet to Manage ML Experiments
Scale is an interesting, often over-simplified challenge in machine learning. Intuitively, most everyone understands that bigger models require large amounts of resources (e.g., large datasets, computational power), but cost is just one piece of ML’s scale problem.
Using Comet Panels for Computer Vision at Pento.ai
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 Researchers using Comet as part of their R&D toolkit.