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

As we close out September, we’ve got some new content worth a look! We’re covering topics like LLM hallucinations, reinforcement learning with human feedback for LLMs, setting up guardrails for LLM safety, and more insights to keep you informed. Enjoy! #generativeai #genai #llm

Pedro T. (from OctoAI (now NVIDIA)) believes the primary LLM challenge in the next 2-3 years will be managing the increasing size of models and optimizing them to run efficiently on existing hardware. — (link to the full interview in the comments) #generativeai #genai #llm

Similar to startups, ML/AI platform teams initially partner with core design partners to gain experience before expanding to support more teams and use cases over time. And as companies scale, new problems arise, requiring pivots in strategy and solutions. That said, ML/AI infrastructure must continuously evolve to keep up with rapidly changing needs and technologies. h/t to Hien Luu for the insight. — (link to the full episode in the comments) #ml #machinelearning #mlops

Our CPO, Aurimas Griciūnas, recently joined The Data Exchange Podcast to discuss the challenges and innovations in training and scaling LLMs. You'll hear about things like: → Going from MLOps to LLMOps → Scale and complexity of LLM clusters and training → Frontier models and training cycles → LLMOps enterprise lessons → Experimentation in agentic systems → What lies ahead Listen here: https://lnkd.in/gkF38DVZ #generativeai #genai #llm

Integration spotlight: MLflow & Neptune ↓ Send your metadata to Neptune while using MLflow logging code. Documentation: https://buff.ly/3uEB7SV #ml #machinelearning #mlops

According to Jeronim Morina (Senior MLOps Engineer at AXA) data security is a top priority and will continue to be a major LLM challenge in the coming years. — (link to the full interview in the comments) #generativeai #genai #llm

Here's what you get with Neptune's free academic research plan: ? Full product functionality ? Unlimited team members ? Unlimited monitoring hours ? 200 GB of metadata storage Not bad, right? If you’re a professor, a student, you belong to an academic research group, or you’re a Kaggler – check out the program here: https://buff.ly/47dzgTU #generativeai #genai #llm #ml #researchers

[New on our blog] LLM Guardrails: Secure and Controllable Deployment by Natalia Kuzminykh TL;DR → The stochastic nature of LLMs makes it impossible to obtain deterministic outputs, leaving prompt as the primary lever—an approach that is often inadequate for ensuring reliable and predictable results. → LLM guardrails prevent models from generating harmful, biased, or inappropriate content and ensure that they adhere to guidelines set by developers and stakeholders. → Approaches range from basic automated validations over more advanced checks that require specialized skills to solutions that use LLMs to enhance control. — (link to the full article in the comments) #generativeai #genai #llm

2 unsolved challenges in the RAG-based LLM space, according to Alison Cossette (from Neo4j): → Understanding the appropriate dataset to use. → Security and data governance within RAG datasets. — (link to the full interview in the comments) #generativeai #genai #llm