Matt Chalmers’ Post

Join AWS and NVIDIA for a BrightTALK fireside chat (Thursday, January 9 at 2:00 PM ET) about how NVIDIA NIM on AWS is optimizing self-hosted generative AI deployment for businesses. Discover how NIM containerized microservices integrate with large language models and custom AI models to enable faster, scalable, and secure generative AI solutions on AWS. Register here https://lnkd.in/eu6S73Ar #AWS #NVIDIA #generativeAI #GenAI #NVIDIANIM #AIdeployment

Join a BrightTALK fireside chat (Jan. 9) on how NVIDIA NIM on AWS is optimizing self-hosted generative AI deployment for businesses. Discover how NIM containerized microservices integrate with large language models and custom AI models to enable faster, scalable, and secure generative AI solutions on AWS. Register here https://lnkd.in/eu6S73Ar for "Optimizing generative AI deployment: unleashing business potential with NVIDIA NIM on AWS."   #AWS #NVIDIA #generativeAI #GenAI #NVIDIANIM #AIdeployment

Partnerships like NVIDIA and Cloudera are already adopting microservices to help organizations across industries securely connect custom models to diverse and large data sources. By using retrieval-augmented generation (RAG) techniques, NeMo Retriever enables AI systems to access richer, more relevant information and effectively bridge linguistic and contextual divides ... check it out!

In a compelling video, Albert Lawrence engages in a deep discussion with Miha Kralj, Global Senior Partner of IBM Hybrid Cloud Services and David Levy, Advisory Technology Engineer at IBM Client Engineering. They delve into the uses and challenges of employing generative AI to write code, offering valuable insights on how to address them. #GenerativeAI #AI #applicationdevelopment #innovation

🚀 Introducing Llama 3.2 on Snowflake Cortex AI 🌐 Meta’s latest large language model collection, Llama 3.2, is now available on Snowflake Cortex AI, enabling enterprises to build generative AI applications efficiently and securely. By using simple SQL functions or REST APIs, businesses can deploy AI models within Snowflake’s trusted environment, ensuring top-level data governance. 🔒 Key Features: ✅ Flexible Model Sizes: Llama 3.2 offers models from 1B to 90B parameters, with 1B and 3B models available now. Larger models (11B and 90B) are coming soon! ✅ Serverless Inference: Seamless serverless AI integration to reduce complexity and boost efficiency. ⚙️ ✅ Advanced Safety: Cortex Guard provides robust content filtering for safe and responsible AI deployment. ✅ Edge and Mobile Optimization: Smaller models (1B, 3B) are optimized for on-device AI in resource-constrained environments, ideal for real-time processing. 📲 🚨 Note: Unfortunately, Llama 3.2 will be restricted in Europe, limiting its availability across the region. Despite this, enterprises worldwide can still leverage Snowflake’s integrated AI platform to rapidly develop and deploy gen AI applications, right-sizing models for their specific needs across industries like retail, manufacturing, and healthcare. 🏥 🛠️ 🛒 🆓 Try Llama 3.2 for FREE until October 8, 2024 and accelerate your AI innovation with Snowflake Cortex AI! #AI #GenerativeAI #Snowflake #Llama #EnterpriseAI #Innovation #Tech Links: Medium: https://lnkd.in/dArTc-Zb Meta: https://lnkd.in/dYQ_42_e Snowflake documentation: https://lnkd.in/d7kY__dR

As enterprises rush to integrate AI into their workflows, they’re faced with significant hurdles: fragmented tools, inefficient development cycles, and the inability to scale their applications. To address these challenges, DataStax has launched its AI Platform, built in collaboration with NVIDIA AI Enterprise. At AWS re:Invent 2024, DataStax's Alejandro Cantarero and Jason McClelland spoke with AIM Research about the platform’s groundbreaking potential, real-world success stories, and its implications for enterprise AI.

As enterprises rush to integrate AI into their workflows, they’re faced with significant hurdles: fragmented tools, inefficient development cycles, and the inability to scale their applications. To address these challenges, DataStax has launched its AI Platform, built in collaboration with NVIDIA AI Enterprise. At AWS re:Invent 2024, DataStax's Alejandro Cantarero and Jason McClelland spoke with AIM Research about the platform’s groundbreaking potential, real-world success stories, and its implications for enterprise AI.

Image generation is evolving at an incredible pace, and productionizing powerful models like Stable Diffusion demands efficiency, scalability, and innovation. Achieving this means relying on cutting-edge hardware to make everything efficient and scalable. That's where Amazon Web Services (AWS)'s Inferentia2 chip comes into play—a game-changing innovation unlocking significant cost savings and unparalleled scalability for models like Stable Diffusion, or any other modern model architectures, including large language models (LLMs). Here's what Inferentia2 brings to the table: ⚡ Scalability: Meet the growing demands of AI image generation. 💰 Cost Optimization: Achieve high-performance inference without breaking the bank. 🎯 Robust Pipelines: Build efficient, production-ready AI systems. At Loka, we've been leveraging the power of Inferentia2 to push the boundaries of AI innovation. Together with my amazing colleagues Zafir Stojanovski and Henrique Ribeiro Delgado da Silva, we've detailed how this powerful chip is transforming our approach to production in our latest blog post. 👉 Read the full blog here: https://lnkd.in/dUYhJ8Er #Loka #AWS #AWSInferentia2 #GenerativeAI #GenAI #StableDiffusion #ImageGeneration #Innovation

The ability to customize existing models or build new ones from scratch is a game-changer. This could be a great way to tailor AI models to our specific needs and data sets. I'll be digging deeper into AI Foundry to see if it can help us streamline our AI development workflow. #nvidia #ai #generativeai #dataarchitect

As enterprises rush to integrate AI into their workflows, they’re faced with significant hurdles: fragmented tools, inefficient development cycles, and the inability to scale their applications. To address these challenges, DataStax has launched its AI Platform, built in collaboration with NVIDIA AI Enterprise. At AWS re:Invent 2024, DataStax's Alejandro Cantarero and Jason McClelland spoke with AIM Research about the platform’s groundbreaking potential, real-world success stories, and its implications for enterprise AI.