A 2-minute demo showcasing how neptune.ai supports teams that train foundation models. Haven't heard about Neptune before? TL;DR: It's an experiment tracker built to support teams that train large-scale models. Neptune allows you to: → Monitor and visualize months-long model training with multiple steps and branches. → Track massive amounts of data, but filter and search through it quickly. → Visualize and compare thousands of metrics in seconds. You get to the next big AI breakthrough faster, optimizing GPU usage on the way. If you want to learn more, visit: https://buff.ly/4cXZGep Or play with a live example project here: https://buff.ly/3WlPVQg
neptune.ai
Software Development
Palo Alto, California 36,774 followers
The experiment tracker for foundation model training.
About us
Neptune is the most scalable experiment tracker for teams that train foundation models. Monitor and visualize months-long model training with multiple steps and branches. Track massive amounts of data, but filter and search through it quickly. Visualize and compare thousands of metrics in seconds. And deploy Neptune on your infra from day one. Get to the next big AI breakthrough faster, using fewer resources on the way.
- Website
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https://neptune.ai
External link for neptune.ai
- Industry
- Software Development
- Company size
- 51-200 employees
- Headquarters
- Palo Alto, California
- Type
- Privately Held
- Founded
- 2017
- Specialties
- Machine learning, MLOps, Gen AI, Generative AI, LLMs, Large Language Models, LLMOps, Foundation model training, and Experiment tracking
Locations
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Primary
2100 Geng Rd
Palo Alto, California 94303, US
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Krańcowa
5
Warsaw, Mazovian 02-493, PL
Employees at neptune.ai
Updates
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Smaller, smarter, safer. Kanika Narang breaks down the future of AI over the next 5 years. → Smaller, multimodal models will drive a more natural integration of technology into everyday life. → Close cooperation between users, engineers, PMs, and UX designers will be crucial for creating impactful applications. → Prioritizing responsible AI and ensuring its reliability will remain essential. — (link to the full interview in the comments) #generativeai #genai #llm
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Hey academic researchers – would you like to optimize the usage of your limited GPUs? You can do it with Neptune, for free. Monitor experiments and compare metrics in real time, reacting quickly to failed runs and divergences. As a bonus, you’ll have all your data in one place, which means easier reproducibility and collaboration with your research group. Check our free academic research program: https://buff.ly/47dzgTU #generativeai #genai #llm #ml #researchers
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We asked Tom Hamer 🦛 from Marqo: What are some of the biggest search engine and recommender system challenges? Here's his take: → Retrieval: unstructured data, such as images, often lack proper labeling. Even when labeled, images hold much more context than text can capture. → Understanding user intent: one-word prompts can be ambiguous. Effective search requires combining retrieval with metadata, like user location or previous interactions. Advancements in AI and LLMs help tackle these challenges by integrating data sources and improving understanding of unstructured data. — (link to the full interview in the comments) #generativeai #genai #llm
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Struggling to compare 100s of training runs on the same chart? Not with neptune.ai. The UI is not only responsive but also super fast, even on a massive scale. Check this example project: https://buff.ly/40ZhN0p
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What’s next for AI in the upcoming 5 years? Some highlights from Prashanth Jayachandran: → GenAI will redefine job roles and workflows as its capabilities expand. → Quantum computing may be critical to advancing AI’s capabilities. → Governments need to adopt flexible policies that can keep up with fast tech changes. → Cross-sector collaboration is essential for managing the impact of AI responsibly. — (Link to the full interview in the comments) #generativeai #genai #llm
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[New on our blog] From Research to Production: Building The Most Scalable Experiment Tracker For Foundation Models Author: Aurimas Griciūnas Reading time: 3 min — (link to the full article in the comments) #generativeai #genai #llm
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Sometimes, raw metrics don’t tell the whole story. With custom expressions, you can turn raw data into actionable insights. Define derivative metrics based on your needs, such as growth rates, weighted averages, or normalized scores. No need for extra tools. You can do it within Neptune’s dashboards or reports. — Custom expressions are another new capability supported in Neptune Scale, our upcoming product release. Test it yourself: https://buff.ly/4eCFUpz #generativeai #genai #llm
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The gen AI challenge isn’t just tech—it’s the people side. Keeping up with fast-paced changes is tough, especially in industries like government, where digital transformation is ongoing. Many companies still miss clear roadmaps for their digital journey. The first step is prioritization: understanding what’s critical to implement and what can wait. — (link to the full interview in the comments) #generativeai #genai #llm
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Calling all Kagglers: Want to increase your chances in ML competitions? Neptune's advanced experiment comparison options and lightning-fast UI are the secret weapons of Kaggle Grandmasters. Track massive amounts of experiments and iterate quickly – all for free. Check out our free program: https://buff.ly/47dzgTU #generativeai #genai #llm #ml #researchers