Customer Stories

Cisco uses Comet's EM platform to version its datasets and log all training and evaluation runs. They use Comet's Model Registry to store and version their best models and having instant access to the full training context.

Ancestry leveraged Comet to quickly process and extract information from 6.6 million historical census images.

NatWest Group used Comet to standardize their ML platform, improve visibility into model management, and foster collaboration across teams.

In this engaging fireside chat, Gideon, CEO of Comet, Eden Dolev, Senior ML-II Engineer, and Alaa Awad Staff ML Software Engineer at Etsy, come together to discuss the development and implementation of an innovative image search product at Etsy.

Prasanna Padmanabhan, ML Platform Director at Netflix, and Gideon Mendels, CEO at Comet, delve into the journey of building an MLOps team at Netflix with the help of Comet. In this engaging session, they will give you a glimpse into the intricate architecture of Netflix’s machine learning infrastructure and share valuable insights into the intricacies of leading MLOps teams in today's ever-evolving tech landscape.

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.

CareRev used Comet to organize their entire machine learning workflow from start to finish, to create a unified system of record for full transparency and reporting.

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

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.

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.

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.

NTT DATA Group implemented Comet to streamline their ML workflows and experienced a significant boost in productivity and improved team morale.