Generative AI World

The Future of Language Models: Why Fine-Tuning Might Fade Away? At a recent panel discussion, How to Finetune Your LLMs and Evaluate Performance on MLOps World: ML in Production summit, panellists delivered many insights about the current state of training LLMS and controversial forecasts about the trajectory of fine-tuning. For several reasons, Meryem Arik's take on fine-tuning large models within a few years will significantly diminish. - First, base models, especially open-source ones, are improving so rapidly that they will largely reduce the need for extensive fine-tuning. - Technologies like prompt engineering will enhance the ability to extract desired outputs from these advanced models. - Secondly, the development of RAG — currently in its infancy with just six months in production — is expected to see major advancements. - Thirdly, the focus is shifting towards control generation, which aims to dictate the format of the model's responses without altering the underlying architecture. These advancements are progressing rapidly and are set to make fine-tuning a minor aspect of enterprise LLM applications. This perspective underscores a significant shift in how we anticipate the evolution of machine learning operations. Do you agree? What is your prediction for fine-tuning? Thanks to the Panel Members for delivering insightful discussion: Savin Goyal: Co-founder & CTO, Outerbounds Shaun H. : Global Head of Solutions Architecture, Cohere Hien Luu: Head of ML Platform, DoorDash Meryem Arik: Co-founder / CEO, TitanML 📺 Watch the Full Video on our YouTube channel: https://lnkd.in/eSKGmVuh

🌟How do you combine all data science resources? Meet The Virtual Feature Store: Simplifying Data Management and Deployment🌟 🔊 Featuring: Speaker: Simba Khadder: Founder & CEO, Featureform 🎓 Talk Title: Feature Stores ≠ Storage 📺 Watch the Full Video to learn how to train and deploy an end-to-end fraud detection model with Featureform, Redis, and Amazon Web Services (AWS): https://lnkd.in/eDTnTk6q

🌟5 Key Takeaways for Successful Product Management | Build, Empathy, and Strategic Choices 🌟 🔊 Featuring: Stefan Krawczyk: CEO & Co-founder, DAGWorks Inc. 🎓 Talk Title: Getting Higher ROI on MLOps Initiatives 🌟 More about the Talk: MLOps is hard because there are so many “things” that you might want to integrate and connect with: -A/B testing, -Feature stores, -Model registries, -Data catalogs, -Lineage systems, -Python dependencies, -Machine learning libraries, -LLM APIs, -Orchestration systems, -Online vs offline systems, -Speculative business ideas, etc. In this talk, Stefan covers five lessons that he learned while building out the self-service MLOps platform for over 100 data scientists at Stitch Fix. This talk is for anyone building their own or buying it all off the shelf. 📺 Watch the Full Video: Learn what to do and what to avoid so you can increase your ROI on MLOps initiative over here https://lnkd.in/eB7x9WK3

🌟How Auto-Scaling and Service Monitoring Amped Up Traffic Handling. TMAP Mobility's Million-User Success Story 🌟 🔊 Featuring: Intae Ryoo Co-founder & CPO, VESSL AI 🎓 Workshop Title: LLM, from Playgrounds to Production-ready Pipelines 🌟 More about the talk: Despite the onset of commercially viable open-source Large Langauge Models, companies are struggling to leverage cutting-edge models like Llama2 and Mistral 7B for production-ready applications. Creating a simple demo page on a personal laptop and training, fine-tuning, and serving multi-billion parameter LLMs on HPC-scale infrastructure - with proprietary enterprise data - involves an entirely different engineering challenge. In this talk, Intae, who co-founded and now leads product development at VESSL AI, will explore how companies can leverage MLOps infrastructure to go from a simple model playground to deploying enterprise-scale pipelines for LLMs. 📺 Watch the Full Video: https://lnkd.in/dUT4-iGQ

OpenAI amazed the world with GPT-4o, the first multimodal AI. Today, we're announcing the the first ever Multimodal AI Guardrails by Aporia. Mitigate Hallucinations, Prompt Injection, any embarrassing AI failures in real-time, before they reach the user. #multimodalai #genai #aiguardrails

Are you looking for an easy, fast, and cost-effective open-source solution for LLM serving? vLLM supports continuous batching, dramatically increasing throughput while reducing latency. It also features an OpenAI-compatible API server, allowing an easy transition for those familiar with OpenAI’s models. Support for various decoding algorithms, including parallel sampling and beam search, further enhances flexibility. GitHub: https://lnkd.in/dnUWhZYM Paper: https://lnkd.in/ggJ7n_8g Project owner: Kaichao You Thanks to Ahmed Tremo for researching this project and Aleksandra Osipova for writing this. #llm #opensource

This powerful open-source project streamlines the entire process, helping you find the optimal RAG pipeline for your specific data needs. Here's what AutoRAG offers: Effortless Data Creation: Easily prepare your data for RAG evaluation. Automated Optimization: Let AutoRAG run experiments and identify the best pipeline configuration automatically. Seamless Deployment: Deploy your optimized pipeline with a single YAML file, compatible with FastAPI servers. GitHub: https://lnkd.in/dccgcAdX PyPI : https://lnkd.in/d93p5RwA Discord : https://lnkd.in/dpj_uCcE Project owner: Jeffrey Kim

Did you miss the Generative AI Tooling & Infrastructure Summit? From RAG system optimization to LLM scaling and fine-tuning, we explored the cutting-edge production stack. Dive into all the sessions now available on our YouTube channel. 🔗 Watch Now: https://lnkd.in/gzV4zzx A big thank you to the participating companies, thought leaders and everyone who attended. Ready to be part of the next summit? 📝 Submit your tool or project here: https://lnkd.in/gnhwuH6u

This week, we focus on finding ways to make LLM operations easier with an open-source project DSPy (Declarative Self-improving Language Programs). We explored services that facilitate the shift from hardcoded prompts to a flexible, programmable strategy. The standout tool we found was DSPy (Declarative Self-improving Language Programs) fully open-source, which transforms language model pipelines into efficient, self-improving systems. 👉 GitHub: https://lnkd.in/dScSBqgu 👉 Paper: https://lnkd.in/dQRESmsN Thanks to Ahmed Tremo and Aleksandra Osipova for helping compile and write this. #llms #opensource

Privacy-Focused AI for Your Documents 🔒 Introducing PrivateGPT (Zylon by PrivateGPT) PrivateGPT is an open-source project (Apache-2.0 license) that lets you leverage the power of Large Language Models (LLMs) to gain conversational insights from your documents, all within the security of your own offline or private environment. Learn More & Get Involved: GitHub: https://lnkd.in/dg4uYBEZ Discord Community: https://lnkd.in/daczy6vn A huge shoutout to Vaibhav P. and Aleksandra Osipova for researching and writing this. #PrivateGPT #LLMs #RAG #DataPrivacy #AI #opensource