Ravi A.

Organizations have insufficient technical capabilities for the speed and scale of their ML model implementations. My colleague Sumit Agarwal just published a very helpful research with a capability view and sample vendors for defining a modular, flexible and scalable MLOps solution. https://lnkd.in/gnB_T9bq #MLOps #LLMOps #AI

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AI leaders have a crucial chance to address risks now and strengthen governance practices before playing catch-up to regulations, notes Kjell Carlsson, Ph.D. of Domino Data Lab. In a recent VentureBeat article, he shares his perspective on California's AI bill veto and why proactive governance is key to the future of AI innovation. Learn more about this addressing this opportunity: https://gag.gl/4WCTkP #AI #AIGovernance #AIInnovation #DataScience

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How do we bring search, workflows, and analytics into the GenAI era? At DevRev we believe it starts with a knowledge graph that can share relationships with LLMs, helping AI understand the connection between your work/people, product, and customer.

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As an AI practitioner, I concur with the article’s emphasis on multi-cloud strategies for AI workloads. It’s essential to use specialized LLMs for proprietary data and public LLMs for broader use cases. The choice between speed and accuracy, alongside cost considerations, plays a crucial role in selecting LLMs. A well-executed multi-cloud strategy not only avoids vendor lock-in but also offers the flexibility to scale and innovate as business needs evolve. #CloudComputing #MultiCloud #ArtificialIntelligence #AIPractitioner #TechTrends

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As companies go multi-cloud in their deployments, we need stand alone MLOps/LLMOps solutions that can deployed across vs being dependent on any one platform. As developers use multiple code generators, we will need security solutions that go across all of them to support developers. As businesses use multiple LLMs/AI models/APIs across the enterprise, we will need stand alone solutions to help identify, implement, and secure data and use cases. These are great opportunities to build next gen independent large software companies.

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GenAI leads to new economies of scale by generating artifacts in bulk or by helping humans to generate them faster. There is plenty of room for commercialized solutions to create new and original value with those #GenAI artifacts. 

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It’s finally here! I’m thrilled to introduce you to Llama 3, our latest generation of models that is setting a new standard for state-of-the art performance and efficiency for openly available LLMs. Key highlights: • 8B and 70B parameter openly available pre-trained and fine-tuned models • Trained on more than 15T tokens, 7x+ larger than Llama 2's dataset! • State-of-the-art performance across industry benchmarks • New capabilities, including enhanced reasoning and coding • 3x more efficient training than Llama 2 • New trust and safety tools with Llama Guard 2, Code Shield, and CyberSec Eval 2. • Training underway on a leading-edge 400B+ parameter model  We’re also releasing a new version of Meta AI powered by Llama 3. We’re making it available in 13 new countries, integrating enhanced search capabilities and adding new ways to experience it on the web at meta.ai. Our latest updates to image generation on Meta AI is state of the art, allowing you to create, animate and share stunning images with a simple text prompt in seconds. We think Meta AI is the world’s most intelligent AI assistant and we can't wait for you to try it and tell us what you think. Read more about it here: https://lnkd.in/eB5yzi8R

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Lifting the Veil on AI's decision making, Vinay Kumar Sankarapu from Arya.ai will share about AI transparency that goes beyond the usual "black box" discussions. In today’s world of complex deep learning models, understanding why a model makes certain decisions is crucial—especially in mission-critical applications. He'll explore DL-Backtrace, a new model-agnostic technique designed to bring transparency to any deep learning model, whether it’s an LLM, computer vision, NLP, or tabular data model. What You’ll Learn: - The importance of explainability for mission-critical deep learning applications - How DL-Backtrace compare to existing techniques like SHAP, LIME, and GradCAM for models including LLMs (Llama 3.2), NLP (Bert), and CV (ResNet) - Real-world benchmarking results showing DL-Backtrace’s performance across varied deep learning architectures - Insights into the current limitations and future possibilities in scaling explainability for large models #ResponsibleAI MLOps World Generative AI World

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I remember when this all started. Faris - invited me to catch up at our favorite spot around Blok M. I had no idea he’d just wrapped up a procedure at Pondok Indah Hospital! (He really knows how to add drama to our catch-ups.) We’d been on separate paths and projects since "the winding down project", but there he was, reminding me of our dream to ride this AI wave together again. It was a plan we’d hatched with Sahal Zain & Gurnoor Dhillon when the world of AI was still mostly theoretical and jargon-heavy. Somehow, the conversation continued with Gurnoor Dhillon while he was waiting for his meeting with someone who would eventually become one of CalvinBall’s key early adopters. It felt like everything was coming together, like a series of seemingly random dots connecting to form a bigger picture. Seeing how far we’ve come as a team at CalvinBall Technologies is truly exciting. What started as theoretical discussions has now evolved into practical Gen AI solutions that are solving everyday problems and hold endless possibilities to shape the industry. 👨‍🚀 👩‍🚀 Here’s to pushing boundaries and achieving new milestones together! Gurnoor Dhillon, Sandeep Ramesh, Faris -, Sahal Zain, Eva Zuliya, Ratu Nadiah Khairunnisa 🚀 #GenAI #AIInnovation #TechJourney #ArtificialIntelligence #TechTransformation #CalvinballTech #Calvinballers

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Success with generative AI can only be unlocked by strategically using an organization’s unstructured data. This is giving rise to an emerging unstructured data stack, which consists of three crucial components: 1️⃣ data extraction and ingestion 2️⃣ data processing 3️⃣ data management In this post, I share more context about the solutions in these spaces and how they are used: LlamaIndex Reducto Tensorlake unstructured.io Vectorize Eventual Databricks Polars Anyscale Pandas Onehouse Tabular (now part of Databricks) Chroma Pinecone Supabase Spiral LanceDB MongoDB Stately Neo4j FalkorDB TigerGraph https://lnkd.in/dT74Yj3C

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💥💥💥 $100K or 100 Days: Trade-offs when Pre-Training with Academic Resources Apoorv Khandelwal, Tian Yun, Nihal V. Nayak, Jack Merullo, Stephen H. Bach, Chen Sun, Ellie Pavlick Abstract Pre-training is notoriously compute-intensive and academic researchers are notoriously under-resourced. It is, therefore, commonly assumed that academics can't pre-train models. In this paper, we seek to clarify this assumption. We first survey academic researchers to learn about their available compute and then empirically measure the time to replicate models on such resources. We introduce a benchmark to measure the time to pre-train models on given GPUs and also identify ideal settings for maximizing training speed. We run our benchmark on a range of models and academic GPUs, spending 2,000 GPU-hours on our experiments. Our results reveal a brighter picture for academic pre-training: for example, although Pythia-1B was originally trained on 64 GPUs for 3 days, we find it is also possible to replicate this model (with the same hyper-parameters) in 3x fewer GPU-days: i.e. on 4 GPUs in 18 days. We conclude with a cost-benefit analysis to help clarify the trade-offs between price and pre-training time. We believe our benchmark will help academic researchers conduct experiments that require training larger models on more data. 👉 https://lnkd.in/d5Vh3ycg #machinelearning

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Have you wondered how can you optimize your compute for building LLMs? Apart from the model architecture and dataset, the other most important component of building LLMs is - infrastructure (GPUs, TPUs, or LPUs)! To train, fine-tune, or run inferencing on your models there are extensive compute requirements along with costs. Hence it is critical to optimize the infrastructure to support your workloads: https://lnkd.in/dJFPwwJz I have authored a detailed blog on how GPU optimization works for training, fine-tuning, or inference for LLMs, or LMMs. PS: At this point, most models being built are multimodal, hence I like using the term LMMs instead of LLMs. If you found the article insightful, share this with your network ♻️ ------------------------------------------------- If you don't want to miss any of my posts, go to my profile (Aishwarya Srinivasan) and click on the 🔔 to get notified about all my posts ❤️

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🚀 𝗗𝗶𝘀𝗰𝗼𝘃𝗲𝗿 𝘁𝗵𝗲 𝗝𝗼𝘂𝗿𝗻𝗲𝘆 𝗼𝗳 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀!  Sharing a quick primer on "Evolution of Large Language Models" that traces NLP's incredible journey from rule-based systems to today's cutting-edge AI models. ✨ Highlights include: 🔹The shift from rules to data-driven methods. 🔹Game-changing neural networks and Transformer architecture. 🔹The rise of GPT models and their amazing potential. Check out the PDF for a deep dive into how these models are reshaping our world. I'd love to hear your thoughts and experiences with LLMs! 💬 📖 Ready to Level Up on AI? ✨ 📚 Want to know AI landscape concisely? ⏰Have no free time to keep up with AI news? I burned 2000+ hours attending courses, seminars, blogs, videos and AI summits. For quick, light-weight informative primers (bi-weekly) that keep you in the loop➰ , Subscribe to SynergIQ WhatsApp Group https://lnkd.in/g9r9PKJr #LearnAndGrow #NewSkills #StayCurious #AIInsights #NLP #AI #LLMs #Innovation #TechTalk

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The emergence of enterprise AI products interweaving with operational workflows is an exciting development. It will be fascinating to see how this integration generates value for customers. https://lnkd.in/dcfYgzrf

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https://lnkd.in/dJW9sZpx SelfExtend introduces a novel approach to expanding the context window of LLMs. It leverages a two-tiered attention mechanism—grouped attention for long-range dependencies and neighbor attention for local interactions—both derived from the model's original self-attention. This method allows seamless context window extension without requiring fine-tuning, and extensive experiments have demonstrated its effectiveness across various benchmarks.

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Fantastic panel on GTM for Enterprise AI. Thanks to fellow panelists for a sharp discussion and to Emergent Ventures for hosting. Anupam Rastogi Ankur Jain Preethy Padmanabhan Chandini M Ammineni Jason Griggs Deb Bardhan #GTM #EnterpriseAI

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Dive into the architecture of OCI’s Generative AI Agent service, Retrieval-Augmented Generation (RAG) to enhance the contextual accuracy of AI agents, addressing the limitations of traditional LLMs and reducing hallucinations.

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