Comet

Comet

Software Development

New York, NY 15,639 followers

Where AI Developers Build

About us

Comet is an end-to-end model evaluation platform built with developers in mind. Track and compare your training runs, log and evaluate your LLM responses, version your models and training data, and monitor your models in production — all in one platform. Backed by thousands of users and multiple Fortune 100 companies, Comet provides insights and data to build better, more accurate AI models while improving productivity, collaboration, and visibility across teams.

Industry
Software Development
Company size
51-200 employees
Headquarters
New York, NY
Type
Privately Held
Founded
2017
Specialties
Machine Learning, Data Science, Developer Tools, and Software

Products

Locations

Employees at Comet

Updates

  • View organization page for Comet, graphic

    15,639 followers

    🛠️ Building a scalable Generative AI platform is challenging, but it doesn’t have to be. Join us and Amazon SageMaker for a technical session on: ✅ The importance of LLM observability in production ✅ How Comet’s Opik can track and monitor your LLMs ✅ Effortlessly setting up Comet within SageMaker AI Partner Apps 📅 Thursday, March 6th | 13:00 - 14:00 EST 🔗 Register: https://lnkd.in/dHBztcRm

    Streamline GenAI system evaluation and observability with Amazon SageMaker and Comet

    Streamline GenAI system evaluation and observability with Amazon SageMaker and Comet

    aws-experience.com

  • View organization page for Comet, graphic

    15,639 followers

    ⭐ Opik officially has 5,000 GitHub Stars! ⭐ Five months ago, we launched Opik out of a growing need from the community to be able to confidently test and trust their LLM applications. Since then, the adoption and engagement we’ve seen has been beyond what we could have imagined. 🚀 Opik trending on GitHub as the #2 top repo 📈 Tens of thousands of users 🤝 Contributions and callouts from users like Andreas Nigg, Jeremy Mumford, Carlos Kemeny, PhDx2, and Prakash Chaudhary 💡 Incredible projects powered by Opik, like Chia Jeng Yang’s PatientSeek, an open-source Med-Legal Deepseek reasoning model We're grateful for the entire community's contributions. Whether you’ve contributed code, shared feedback, or spread the word, we’re excited to keep building with you 🦉

  • View organization page for Comet, graphic

    15,639 followers

    🎉 Proud to be a community sponsor at the AI Tinkerers – NYC x OpenAI Hackathon this weekend! 📣 If you're attending, be sure to say hello to Claire L., who will be representing Comet and diving into our open‐source LLM Eval framework, Opik. Can't wait to see what teams build 🤖

    View profile for Joe Heitzeberg, graphic

    Working to Expand AI Tinkerers Globally

    AI Tinkerers is cooking this weekend around the world! 🌎 AI Tinkerers - NYC x OpenAI 🌍 AI Tinkerers - Paris x Anthropic 🌏 AI Tinkerers - Singapore x AWS 🧘 🧘 🧘 🧘 🧘 🧘 🧘 🧘 🧘 🧘 🧘 🧘

  • Comet reposted this

    View profile for Jacques Verré, graphic

    Head of Product @ Comet ML

    🚀 Opik Weekly Changelog 🚀 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁 𝗼𝗳 𝘁𝗵𝗲 𝘄𝗲𝗲𝗸: Multiple external contributions have been released this week ! Opik is gaining momentum even faster than I expected ! One of the best parts of working on Open-Source projects are community contributions, not only do they improve the overall features of the product but they also often improve the quality of the product significantly. From day one we decided to prioritize reviewing user contributions quickly and we couldn't be happier we did ! We also released: • Performance improvements for workspaces with over 100 million traces • Added support for cost tracking when using Gemini models • Added diffing of prompt versions • Improved support for Ragas metrics in `evaluate_*` functions in the SDK • Added support for Bedrock `invoke_agent` API And as always, thank you to all of Opik's external contributors including Jeremy Mumford, Rahul Kadam, Prakash Chaudhary, @demdecuong and @jeffy !

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  • View organization page for Comet, graphic

    15,639 followers

    LLM-as-a-judge evaluators may seem simple on the surface, but implementing them in real-world applications is challenging. They excel at tasks that are difficult to quantify with traditional heuristic metrics like hallucination detection, creative generation, content moderation, and logical reasoning. But evaluating a model across multiple metrics often requires creating separate LLM-as-a-Judge pipelines for each metric and combining their outputs. G-Eval simplifies this process by consolidating evaluations into a single metric, effectively providing the model with a unified scorecard. 👉 Learn more about G-Eval, including how to use it out-of-the-box with Opik, in this new article from Abby Morgan: https://lnkd.in/eF-iBMzv #GenerativeAI #ArtificialIntelligence 📣 Also, a big shoutout to the authors of the original G-Eval paper: Yang Liu, Dan Iter, Yichong Xu, Shuohang Wang, Ruochen Xu, Chenguang Zhu, Yang Liu, Microsoft Cognitive Services Research

    G-Eval for LLM Evaluation

    G-Eval for LLM Evaluation

    comet.com

  • View organization page for Comet, graphic

    15,639 followers

    🔨 Building great open-source tools is hard. Building a scalable, open-source tool that can monitor production LLM workloads? That’s even harder. Our engineering team spent much of 2024 tackling this challenge. Andrés Cruz, Principal Engineer at Comet and lead engineer on the project, breaks down the key architectural decisions and trade-offs that shaped Opik in this blog post. 🔥 👀 Take an inside look at the project 👇

    Building Opik: A Scalable Open-Source LLM Observability Platform

    Building Opik: A Scalable Open-Source LLM Observability Platform

    comet.com

  • View organization page for Comet, graphic

    15,639 followers

    💡 Building LLM apps with Dify? Meet Opik 👋 🦉Opik, our open-source LLM evaluation tool, now integrates with Dify. While Dify makes it easy to build LLM-powered apps, Opik makes advanced evaluation possible with tools like trace annotation and log evaluation. Now you can trust your LLM outputs beyond “vibe-y” guesswork — all while staying in the tools you know and love. 📚 Learn more and get started here: https://lnkd.in/d8jNA2p3

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  • View organization page for Comet, graphic

    15,639 followers

    AI systems aren't software pipelines—and that's the challenge. Non-deterministic models need observability to perform predictably. Some great thoughts from Aishwarya here. Proud to have leaders in the space recommending Opik, our open-source LLM evaluation framework.

    View profile for Aishwarya Naresh Reganti, graphic

    Tech Lead @ AWS | Lecturer | Advisor | Researcher | Speaker | Investor | CMU LTI Alumni |

    ⛳ Deploying AI systems is fundamentally different (and much harder, IMO) than software pipelines for one key reason: AI models are non-deterministic. While this might seem obvious and unavoidable, shifting our mindset toward reducing it can make a significant impact.  The closer you can get your AI system to behave like a software pipeline, the more predictable and reliable it’ll be. And the way to achieve this is through solid monitoring and evaluation practices in your pipeline—a.k.a, observability. Here are a just a few practical steps: ⛳ Build test cases: Simple unit tests and regression cases to systematically evaluate model performance. ⛳ Track interactions: Monitor how models interact with their environment, including agent calls to LLMs, tools, and memory systems. ⛳ Use robust evaluation metrics: Regularly assess hallucinations, retrieval quality, context relevance, and other outputs. ⛳ Adopt LLM judges for complex workflows: For advanced use cases, LLM judges can provide nuanced evaluations of responses. A great tool for this Opik is by Comet, an open-source platform built to improve observability and reduce unpredictability in AI systems. It offers abstractions to implement all these practices and more. Check it out: https://lnkd.in/gAFmjkK3 Tools like this can take you a long way in understanding your applications better and reducing non-determinism. I’m partnering with Comet to bring you this information.

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Funding

Comet 5 total rounds

Last Round

Series B

US$ 50.0M

See more info on crunchbase