Strategies for Multimodal Content Creation

One very powerful thing you might not be doing with AI yet (that you should): Use video with your AI tools and prompts. With something like Google AI Studio, you can unlock some wild multimodal capabilities. One example: Today, I recorded myself stumbling through the interface of a new AI tool I was learning about and experimenting with. During it, I talked through what I was seeing, what features looked interesting, and all the comments and questions I had about the tool. Then I uploaded the 30-min video to Google AI Studio and prompted Gemini to help me script it all out into a fully polished demo… (In case I want to publicly teach it somebody.) It analyzed the video with my commentary, then provided me with great suggestions on: - Features to highlight and dwell on - “Wow” moments to consider showcasing - A potential structure for a more formal demo - And script ideas to clearly explain the tool I’m not doing anything new or exciting here: I’m using AI to augment my work like I always do. But the TYPE of stuff I can now feed it makes all the difference in WHAT I can actually do. So, if you haven’t tried out getting more out AI by using video, I would highly recommend it. Multimodal isn’t just a buzzword. It’s a cheat code.

𝐖𝐡𝐚𝐭 𝐂𝐡𝐚𝐭𝐆𝐏𝐓 𝐝𝐢𝐝 𝐟𝐨𝐫 𝐭𝐞𝐱𝐭, 𝐍𝐈𝐌𝐬 𝐚𝐫𝐞 𝐝𝐨𝐢𝐧𝐠 𝐟𝐨𝐫 𝐯𝐢𝐬𝐢𝐨𝐧 + 𝐥𝐚𝐧𝐠𝐮𝐚𝐠𝐞. NVIDIA 𝘕𝘐𝘔𝘴 𝘢𝘳𝘦 𝘳𝘦𝘴𝘩𝘢𝘱𝘪𝘯𝘨 𝘩𝘰𝘸 𝘝𝘪𝘴𝘪𝘰𝘯-𝘓𝘢𝘯𝘨𝘶𝘢𝘨𝘦 𝘔𝘰𝘥𝘦𝘭𝘴 𝘨𝘰 𝘵𝘰 𝘮𝘢𝘳𝘬𝘦𝘵. Forget complex deployment stacks. Think containerized brilliance that just works. But unlocking their full power still takes planning. Especially in enterprise-grade staging. You can 𝐬𝐭𝐚𝐜𝐤 𝐦𝐨𝐝𝐞𝐥𝐬 𝐥𝐢𝐤𝐞 𝐜𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧, 𝐝𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧, 𝐚𝐧𝐝 𝐕𝐋𝐌𝐬 all in one pipeline. Here’s what it takes to make NIMs production ready: 𝐀𝐥𝐢𝐠𝐧 𝐨𝐧 𝐭𝐡𝐞 𝐕𝐋𝐌 𝐮𝐬𝐞 𝐜𝐚𝐬𝐞 ↳ Choose detection, captioning, or multimodal understanding ↳ Clarify latency vs. accuracy tradeoffs ↳ Secure business stakeholder buy-in 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐞 𝐦𝐨𝐝𝐞𝐥 𝐜𝐨𝐧𝐭𝐚𝐢𝐧𝐞𝐫𝐢𝐳𝐚𝐭𝐢𝐨𝐧 ↳ Use pre-built NIMs for faster provisioning ↳ Pruning models ↳ Leverage Triton inference for performance ↳ Enable A/B testing across endpoints 𝐒𝐞𝐜𝐮𝐫𝐞 𝐢𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 𝐟𝐨𝐫 𝐬𝐭𝐚𝐠𝐢𝐧𝐠 ↳ Provision GPU nodes via Kubernetes ↳ Integrate with MLOps pipelines ↳ Establish rollback and observability 𝐄𝐧𝐚𝐛𝐥𝐞 𝐦𝐮𝐥𝐭𝐢𝐦𝐨𝐝𝐚𝐥 𝐝𝐚𝐭𝐚 𝐟𝐥𝐨𝐰𝐬 ↳ Support image, video, and text inputs ↳ Preprocess and enrich metadata ↳ Store embeddings for retrieval 𝐌𝐨𝐧𝐢𝐭𝐨𝐫 𝐚𝐧𝐝 𝐚𝐝𝐚𝐩𝐭 𝐢𝐧 𝐫𝐞𝐚𝐥 𝐭𝐢𝐦𝐞 ↳ Log prompt inputs and outputs ↳ Track vision-text alignment errors ↳ Iterate for regulatory and user feedback ↳ Chuck the frames Not just building smarter AI. Now staging models to scale intelligently. ♻️ Repost to your LinkedIn followers if AI should be more accessible and follow Timothy Goebel for expert insights on #AI & #innovation. #VisionLanguageModels #NvidiaNIMs #EnterpriseAI #MLOps #GenAI

Ever wished your #RAG solution could handle text and images seamlessly with a powerful embedding representation? 📚🖼️ Inspired by Anthropic's latest research on #ContextualRetrieval, I dove deep into building a Multimodal Retrieval-Augmented Generation (RAG) pipeline using #AzureAISearch, #AzureOpenAI, LlamaIndex, and Arize AI Phoenix. Here's what you'll discover in my latest blog: 1️⃣ 𝗕𝘂𝗶𝗹𝗱 𝗮 𝗠𝘂𝗹𝘁𝗶𝗺𝗼𝗱𝗮𝗹 𝗥𝗔𝗚 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲: Integrate text and images from complex documents for richer, more relevant search results. 2️⃣ 𝗘𝗻𝗵𝗮𝗻𝗰𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆: Use context-aware embeddings to give your GenAI solution the bigger picture. (No Pun Intended) 3️⃣ 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗲 𝗮𝘁 𝗦𝗰𝗮𝗹𝗲: Leverage Arize Phoenix to observe, trace and evaluate different query engines. Full blog here: https://lnkd.in/e9DnVYpY #GenerativeAI #AzureAI #MSFTAdvocate #LlamaIndex #MultimodalRAG