AI/R’s Post

In my last update, I shared some insights into developing a chatbot using Anthropic Claude on Amazon Bedrock, enriched by Amazon Bedrock Agents. The knowledge base was powered by Amazon OpenSearch (the recommended AWS option) with dynamic actions handled through AWS Lambda – a setup designed to leverage cutting-edge technology for an intelligent and responsive chatbot. However, I encountered an unexpected challenge regarding the cost model of the serverless option in Amazon OpenSearch. Despite the serverless promise, I was surprised to find charges accruing hourly from the moment it was enabled. This raised concerns about the feasibility of maintaining such costs for a personal Proof of Concept (PoC). 🙌 I want to extend my gratitude to the AWS support team for their exemplary service. They promptly addressed the issue and provided a full refund for the incurred costs. Their responsiveness and support were nothing short of excellent. Given the cost implications for my PoC, I decided to give a try to Pinecone (pinecone.io) – which is one of the four vector databases available for BedRock Agents, perfect for building high-performance, vector-based applications and services. I'm happy to share that I've successfully integrated Pinecone into my project, taking advantage of their Free Tier offering. The integration process was straightforward, thanks to their simple and clear instructions - https://lnkd.in/dACE5xpY -. Pinecone not only meets my project's needs but also aligns with my goal of cost-effective innovation. I look forward to sharing more updates as this project evolves!

Expect agentic AI to take center stage in 2025. AWS newly introduced Amazon Bedrock Flows and Multi-agent Orchestration Tools that simplify agentic workflow development, empowering organizations to create more sophisticated and adaptive apps with less effort. 🚀 [Amazon Bedrock Flows](https://lnkd.in/gwsykFYW) – a managed service * No-code visual workflow builder * Seamless AWS service integration * Advanced multi-agent conversation management * Visual component assembly * Serverless infrastructure * Built-in traceability * Versioning and A/B testing capabilities 🔥[Multi-Agent Orchestrator](https://lnkd.in/g-5FX8m4)  - a open source with Apache 2.0 license * Intelligent intent classification * Flexible agent responses * Context management * Extensible architecture * Universal deployment * Pre-built agents and classifiers 🎈[Agentic-orchestration](https://lnkd.in/gT2nAdB4) - a use case solution under Apache 2.0 license * Multi-agent collaboration * Combination of AWS and open-source tools * Enhanced reasoning capabilities * Support multimodal agentic tools * Decoupling from foundation models

See how Microsoft plans to turn its investments in OpenAI's generative AI technology into revenue; learn about a newly discovered security flaw that impacts just about everybody running Fluent Bit, a widely used piece of open-source software; and get caught up on the latest funding rounds in enterprise tech.

🚨 AI Reshapes Observability: Are You Prepared? As AI integration accelerates in observability tools, we face new challenges and new opportunities. In this week's digest: • New Relic's AI-powered observability platform launch • #Flatcar Container Linux joining #CNCF Incubator • Scaling Prometheus with Prathamesh Sonpatki • Observability Maturity Framework insights with James Fischer Jr. Discover how these trends impact your role. Read now and stay ahead! https://lnkd.in/ej7sQzpi #Observability #ArtificialIntelligence #CloudNative #DevOps #SRE

🎯Do you thrive on staying ahead of the curve and learning from engineering experts? Then mark your calendars because we've got something special coming up with our FastTrack Azure engineers next week!   April 22nd: Load Balancing Azure OpenAI instances using APIM and Container: Learn how to effectively load balance Azure OpenAI instances to mitigate throttling challenges (TPM & RPM limitations) using API Management custom policies. https://lnkd.in/dFHN_4d4 April 23rd: Azure OpenAI Application Identity & Security: Learn how to enable authentication and authorization in their generative AI application using Entra ID https://lnkd.in/dBNrRZht April 24th: Monitoring Azure OpenAI: Learn about concepts like Token Usage, Quota, and Response Times. As we focus on monitoring for resiliency, performance, and response times, we will discuss Metrics, Dashboards, and Alarms. Finally, a detailed dive into diagnostic settings and log analytics, including the use of Kusto. https://lnkd.in/dv9-3XAx May 1st: Chat with your data: Learn how to use Azure OpenAI and Azure AI Search for creating intuitive, data-driven chat interfaces including tweaking chunking, embedding skillsets, and customization of the chat application page for brand alignment. https://lnkd.in/d9J5xAFm

We’ve been sitting on some exciting news and we’re finally ready to share it. Lambda Inference API is live. https://lnkd.in/g6PqWfyu As a company, we exist to make ML engineering a little bit easier. As an AI lab ourselves, we don’t just offer compute, we help our customers achieve product-market-fit and then help share it with the world. Lambda’s software platform is designed to be a sandbox for ML engineering teams, a place for them to access resources and do their work from start to finish. For a long time now, Lambda has been the best place to find and launch accelerated VMs optimized for AI/ML. We deepened this capability over the summer by enabling self-service virtual clustering using our proprietary partitioning software (1CC). And now, we’re giving our customers a place to publish their models once they’re production-ready. Whether a customer is a startup or a large enterprise, the purpose of model development is to create something useful and share it with others. As a community-driven platform, we see a lot of amazing projects being created on Lambda. Now, we’re opening the aperture by offering customers a global distribution network to get their projects into the hands of other researchers & developers. So if you’re a developer, come build and post your models on Lambda Cloud. If you’re looking for affordable pretrained models to integrate into your app, come check out our inference API. We’ve got 12 models available now at insanely low prices. (More to come!) Long live Lambda. 🌙

𝗨𝗻𝗹𝗼𝗰𝗸 𝘆𝗼𝘂𝗿 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗽𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹 𝘄𝗶𝘁𝗵 𝗖𝗼𝗱𝗲𝘅𝗔𝘁𝗹𝗮𝘀! 🚀⁣ ⁣ Did you know that developers spend 20% of their time writing documentation? 📊 CodexAtlas automates this process, freeing up valuable time for your team to focus on innovation and feature delivery.⁣ ⁣ 👀W𝗶𝘁𝗵 𝗖𝗼𝗱𝗲𝘅𝗔𝘁𝗹𝗮𝘀:⁣ ⁣ 𝟭. You increase your productivity, saving up to 32 hours per month.⁣ 𝟮. You speed up the onboarding of new team members, as documentation is always up to date.⁣ 𝟯. You reduce risks: Project knowledge is shared, not isolated.⁣ 𝟰. Increased focus, as developers can concentrate on creating new features.⁣ ⁣ 🔐𝗞𝗲𝘆 𝗳𝗲𝗮𝘁𝘂𝗿𝗲𝘀:⁣ ⁣ - 𝗥𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝘂𝗽𝗱𝗮𝘁𝗲𝘀⁣ - 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻⁣ - 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗰 𝗥𝗘𝗔𝗗𝗠𝗘𝘀⁣ - 𝗨𝘀𝗲 𝗰𝗮𝘀𝗲 𝗱𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝘀⁣ - 𝗖𝗼𝗱𝗲 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗶𝗼𝗻⁣ ⁣ Join the revolution with over 136,840+ documented files - try CodexAtlas for free today! ⁣ https://lnkd.in/dV_xbmxs #CodexAtlas #AI #SoftwareDevelopment #AutomatedDocumentation #Productivity #TechnologyInnovation⁣

👉Deploy #Gemma7B with UbiOps! New tutorial available ⬇️ In this guide, we explain how to: ✅ Create a UbiOps trial account ✅ Create a code environment ✅ Retrieve your Hugging Face token and accept Google’s license ✅ Create your Gemma deployment ✅ Create a deployment version with a GPU instance type ✅ Make an API call to Gemma 7B! https://bit.ly/3Q3CO3W #AI #MachineLearning #MLOps #Gemma7b #LLM #UbiOps #Guide #Deployment #tech

Lambda Inference API is Live..

If you are building an application that uses OpenAI don't miss our sessions next week on Load Balancing and Monitoring with Azure OpenAI. Learn how to use OpenAI across multiple regions to get additional quota and faster response times. A must have if you plan to take advantage of Provisioned Throughput to improve model response times.