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

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

Would your team like to start using neptune.aii, but you’re only interested in the on-prem version? No problem. Many customers deploy Neptune on their own infrastructure or private cloud. Feel free to get in touch with us. And if you want to explore the ground first, check our on-prem documentation: https://buff.ly/3Znk7gL We talk about: → Requirements for deploying and running Neptune on your own infrastructure; → Installation procedure; → Info about compatibility with Neptune client packages; → Frequently asked questions; → And more. #ml #machinelearning #mlops

What is the right scale to introduce a vector database? TL;DW → There's no one right scale to introduce a vector database per se. → In general, it’s recommended to use a vector search library when handling less than a million vectors. → When managing over a million vectors and/or looking for features like filtered, scalar, metadata search, higher QPS, lower latency, etc., it’s worth considering switching to a vector database. → As application complexity and metadata grow, the transition from a vector search library to a vector database becomes necessary. h/t to Frank Liu for the insight. — (link to the full episode in the comments) #ml #machinelearning #mlops

Filippo Maria Bianchi from UiT- The Arctic University of Norway already uses Neptune for free, together with his students. Anyone else interested in our free plan for academic research? Check the program: https://buff.ly/47dzgTU #generativeai #genai #llm #ml #researchers