neptune.ai

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

Austin Varela from Weill Cornell Medicine already uses Neptune for free for his research work. Anyone else interested in our free plan for academic research? Check the program: https://buff.ly/47dzgTU

New integration alert: neptune.ai + Flyte & Union.ai ?? You can now use Neptune within Flyte, as the plugin configures links between the two platforms. Kudos to Thomas J. Fan, Shalabh Chaudhri, and the rest of the Union team for creating the integration! Dive into the integration example here: https://buff.ly/4dTFHxm — This is one of our community-developed integrations. See others in our docs: https://buff.ly/42NwRvl

>> Calling all AI researchers << Got a paper on foundational model training? This is your chance to win a NeurIPS 2024 ticket and spotlight your research. Join us for a 30-minute video interview where we’ll ask you to explain your work at 3 different levels of complexity: ? Level 1: for a 1st or 2nd grader – simplify it as much as possible. ? Level 2: for a high school or university student – make it accessible with a minimal understanding of the subject. ? Level 3: for a peer AI researcher – go deep into the technical details. Get all the giveaway details here: https://buff.ly/3zY3Jcp #NeurIPS #llm #largelanguagemodels #generativeai

[New on our blog] Observability in LLMOps: Different Levels of Scale Author: Aurimas Griciūnas Reading time: 3 min — (link to the full article in the comments) #generativeai #genai #llm

Finding the source of an issue is frustrating when you don’t have a reproducible pipeline. With all your metadata in one place, you can easily roll back to the last working stage of your model — and fix the step that broke. You can use neptune.ai to do that. Track things like: ? Datasets ? Dependencies ? Parameters and model configuration ? Source code ? And more. Basically, anything else you need to reproduce your training. We gathered some resources you might need in our docs: https://buff.ly/3SRKxnx #ml #machinelearning #mlops

Building an effective internal ML platform begins with the right users. Don’t fall into a trap. Teams with the biggest problems do not necessarily solve the most critical business problems. Ensure you’re investing in users close to business priorities. h/t to Piotr Niedzwiedz for the insight. — (link to the full episode in the comments) #ml #machinelearning #mlops

You know what’s one of the top 5 reasons why ML teams are looking for MLflow alternatives? Lack of proper collaboration features. Here’s what we hear on the calls: “We tried MLflow. But the problem is that they have no user management features, which messes up a lot of things." Often, when people look for a way to track and organize experiments, they come across MLflow and try it right away. No wonder they do it since the tool is free to download and it’s open-sourced. But if you’re working in a team, cloud SaaS tools are solid, longer-term solutions. neptune.ai was built with team collaboration in mind, so it’s packed with collaborative features — like customizable workspaces, persistent shareable links, or access management. Check our website to read more about them: https://buff.ly/3yKpEmo #ml #machinelearning #mlops

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

MLOps is closely related to DevOps, with similar practices but tailored for ML workflows. With its unique challenges, LLMOps can be seen as an extension or additional layer on top of MLOps. Understanding and adapting to these changes is crucial for staying ahead. — (link to the full episode in the comments) #mlops #devops #llmops #ml