Vinod Devan’s Post

It was a crazy lesson today, I learned how to set up Auto Scaling Groups in AWS. It's fascinating how you can use Machine Learning to predict your future traffic and have it scale your instances automatically. #1dayatatime

Excellent course!

🙂 Humbled to have been included in Werner Vogels's keynote today. This is my free Amazon Bedrock Agentic course created along with Andrew Ng and DeepLearning.AI. This course teaches you to build and deploy a serverless agentic application. You’ll learn to create agents with tools, code execution, and guardrails. The serverless setup is ideal for agents that might need to access many tools or APIs on demand. By the end, you will have built a sophisticated AI agent capable of handling real-world customer support scenarios, fully serverless, and ready to scale. https://lnkd.in/gfn-W--H Amazon Web Services (AWS)

If you are excited agents and want to dive deeper, checkout this free course from Mike Chambers, Andrew Ng, and DeepLearning.AI. It’s a resource for anyone who has basic Python knowledge and wants to build an agentic application for business or personal use. #reinvent #genai

🚨 Batch Inference for Embeddings! 🚨 When you work with embeddings, batch inference is crucial before loading them in some sort of vector database such as Pinecone! With Sagify, you can choose from a variety of open-source embedding models to trigger batch inference jobs on your own Amazon Web Services (AWS) account. Coming next: We're working hard to include embedding models from mixedbread.ai on Sagify as their models produce state-of-the-art results for many ML tasks! Try Sagify now if you're working on LLMs and Embeddings https://lnkd.in/eGndD5_P

In AWS re:Invent In 2023, we are announcing general availability of knowledge bases for amazon Bedrock. With Knowledge Bases for amazon Bedrock, you can securely connect base models (FM).... #Amazon #bases #Bedrock #document #Knowledge #Questions #Simplify #single

AWS re:Invent 2024 came with 𝟴𝟮𝟬 𝘀𝗲𝘀𝘀𝗶𝗼𝗻𝘀 𝗳𝗼𝗿 𝗚𝗲𝗻𝗔𝗜/𝗠𝗟 🤖 - that's an overwhelming 34% out of the 2378 total sessions! ✨ Regarding the announcements, we've created a small summary for the (opinionated) top 10 only considering GenAI & ML. As in the last time, visually prepared by our magician Eduardo Rabelo 🪄 P.S.: If you're an engineer and you're as AWS obsessed as we are, consider joining our bi-weekly newsletter 💌 https://lnkd.in/eKaRd8sw If you missed our initial post, check it out here or in our blog ✍️ https://lnkd.in/eYrmQZ6H

Seeing these ML and GenAI highlights (1) and according to other announcements, I can notice a parallel movement regarding DataOps: a trend to simplify and even eliminate the need for ETL proccess. 💥

Interested in GenAI Prompt Chaining on AWS ? Then checkout my two patterns on Serverlessland. Here I demonstrate two simple examples using Amazon API Gateway (REST) https://lnkd.in/gtFg_hmq & AWS AppSync (GraphQL) https://lnkd.in/g_K9JRjM Both invoke an Express state machine synchronously and utilize AWS Step Functions intrinsic functions to chain two prompts, which are then used to invoke the Amazon Bedrock language model. These no-code examples showcases how the results from the first prompt can be used to provide context for the second prompt, allowing the language model to deliver a highly-curated response. By chaining these prompts, the system can leverage the capabilities of the LLM to generate more meaningful and contextual outputs.