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

Where is genAI taking us in the next 5 years? Here are the highlights from the interview with Joshua Rubin from Fiddler AI ↓ • A shift in algorithm design: we may see breakthroughs that make AI faster, cheaper, and less power-hungry. • Personal assistants will evolve into agentic systems, handling complex workflows and adapting to user needs with memory-driven precision. • Human-AI communication will improve—think natural conversation flow, visual cues, and interfaces that feel less robotic. • AI will become deeply embedded into everyday tools, turning fragmented processes into smooth, automated workflows. — (Link to the full interview in the comments) #generativeai #genai #llm

Getting others on the same page shouldn’t take effort. Easily share your work with persistent URLs to the Neptune app. Link directly to experiments, dashboards, or reports, and be sure others see exactly what you want them to see. #generativeai #genai #llm

For builders at heart, the rapid growth in the AI tooling landscape is nothing short of inspiring. Every part of the tech stack is transforming, especially with evaluation tools pushing LLM reliability forward. There’s no better moment to innovate. — (link to the full interview in the comments) #generativeai #genai #llm

Cradle is an amazing biotechnology company leveraging ML and generative AI to accelerate protein engineering. By combining computational methods with experimental data, Cradle optimizes protein structures and functions, dramatically reducing the time and cost of traditional protein engineering processes. We’re super proud that neptune.ai supports Cradle’s innovative work! Take a look at the full case study here: https://buff.ly/4gCpyhy #generativeai #genai #llm

Hey, professors and students! Do you know that Neptune’s free for academic research? No backend setup required – just sign up, add a few lines of code to your training script, and you’re all set. Teach and learn best practices for tracking real-life projects. Check out our free program: https://buff.ly/47dzgTU #generativeai #genai #llm #ml #researchers

Tom Hamer, CEO of Marqo, offers his outlook on the future of LLMs over the next 5 years. TL;DW: → LLMs have more use cases than people think, especially in search and recommendations powered by embedding models. → Innovations in LLMs will trickle into robotics, bringing more robots into our daily lives. → LLMs won't just be chat assistants; they'll increasingly automate back-office tasks like sales. — (link to the full interview in the comments) #generativeai #genai #llm

Every hour you spend downloading data from an experiment tracker and plotting it elsewhere is an hour lost to innovation. With @neptune.ai you can visualize even the largest training jobs. Whether it’s 1k or 1mln data points, charts load quickly, and spikes or anomalies are instantly visible. #generativeai #genai #llm

[New on our blog] How to Build and Evaluate a RAG System Using LangChain, Ragas, and neptune.ai by Isaac Chung TL;DR → LangChain provides composable building blocks to create LLM-powered applications, making it an ideal framework for building RAG systems. Developers can integrate components and APIs of different vendors into coherent applications. → Evaluating a RAG system’s performance is crucial to ensure high-quality responses and robustness. The Ragas framework offers a large number of RAG-specific metrics as well as capabilities for generating dedicated evaluation datasets. → Neptune makes it easy for RAG developers to track evaluation metrics and metadata, enabling them to analyze and compare different system configurations. The experiment tracker can handle large amounts of data, making it well-suited for quick iteration and extensive evaluations of LLM-based applications. — (link to the full article in the comments) #generativeai #genai #llm #rag

Dean Wampler's (IBM Head of Technology) top 2 genAI predictions for the next 5 years: → Chatbots may remain popular for creative tasks, but the buzz around them will settle. → Domain-specific models will dominate—designed for focused enterprise problems, solving practical challenges. — (Link to the full interview in the comments) #generativeai #genai #llm