Amazon Web Services (AWS)’s Post

A very clear overview of AWS Sagemaker Hyperpod and our usage of it.

Just finished the course “Building Intelligent Chatbots on AWS”

With Amazon Bedrock Agents and Amazon Bedrock Knowledge Bases going GA during re:Invent you can now create intelligent agents that can perform tasks for your customers using natural language and your enterprise systems and data sources. And what is better than deploying them in your infrastructure using AWS CDK? 😄 If you are as big of a fan of AWS CDK as myself and interested in trying out Agents and Knowledge Bases with it, check out my custom construct on ConstructHub with deployment examples - https://lnkd.in/gy6cWbMZ Definetely a work a progress but thought it could be useful for others as a starting point. Also, make sure you explore code samples in the aws repository, where myself and other AWS SAs show examples on how Agents and Knowledge Bases could be used - https://lnkd.in/gn4QreJK #aws #reinvent2023 #generatieveai #bedrock

What can you do with Amazon Bedrock Agents? A lot - and when you enable code interpretation, you can do even more. Code interpretation gives the agent a sandboxed playground to run code in, allowing it to write code to: do math, process files you send it, produce its own files and send them back. It's in preview now. I got to build the AWS github sample notebook for it :) Here's the code sample: https://lnkd.in/g364jaaZ Here's the preview announcement: https://lnkd.in/g6rcD4WD

Exciting news! AWS has announced the availability of the Cohere Command R fine-tuning model on Amazon SageMaker. This new addition empowers enterprises to leverage large language models for a variety of applications. The blog post explores the benefits of fine-tuning the model and the process of accomplishing it with Cohere Command R. It also provides a solution overview and details on the steps for fine-tuning. Start building with Cohere’s fine-tuning model in SageMaker today. Read the full article here: https://ift.tt/veOgnxC

I completed a tutorial for AWS Step Functions and State Machines this week and now posted the article at https://lnkd.in/gBjmkETy

Although ML models get all the love and attention, a model on its own is not something that generates value. For an ML model to become an ML solution, it needs to be deployed into the real world (i.e., where value comes from). 👉 In my latest YouTube video, I describe a simple 3-step strategy for deploying ML models so they can readily be integrated into web applications. Here's the 3-step strategy 👇 1) Wrap model in API via FastAPI 2) Containerize the API using Docker 3) Deploy the Docker image using AWS Elastic Container Service Check it out and let me know what you think 😁 https://lnkd.in/gmhPpYZS

🚨 What is in the last AWS Serverless ICYMI (in case you missed it) quarterly recap of Q2 2024? 🐍 Lambda: Run code AFTER returning responses. Async work without blocking! 🔌 CircuitBreaker for Lambda: Prevent cascading failures using DynamoDB. 🧪 Step Functions TestState API: Test individual states independently. Faster iterations! 🤖 Bedrock Converse API: Simplified multi-turn conversations with FMs. One API to rule them all. 🧠 Amazon Q Developer (GA): AI assistant in AWS Console & IDEs. Understands YOUR AWS resources! ⏱️ API Gateway: Integration timeouts beyond 29 seconds. LLM-friendly at last! ⚡ AppSync: Async Lambda invocations. Decouple API responses from long-running ops. 📊 ECS Service Connect: Custom metrics for proactive scaling. Right-size with precision! Which feature solves your biggest challenge? Or what's missing for your serverless nirvana? Remember: 90% of AWS features come from YOUR feedback. Speak up, shape the future! Drop your thoughts in comments 👇 Let's build better serverless together! #AWS #Serverless #DevOps #CloudNative

Developers, If you think that there is no scope that your job is in danger because of Chat-gpt or Gen-AI ... then just watch this video once. https://lnkd.in/gFSHG2yx Let me explain it as a summary of this video in some important point: 1. Here you will that it can raise PR to production for upgrading your project from java's one version to another version(say 11 to 17).(whoever has done it previously ,can easily understand its complication ) 2. You will also see that just by giving a list of feature to your file as a comment,it can write a code for that feature to raise PR to master. 3. You will also see that it can even resolve bugs in your code . 4. As another point ... it can write a code to use AWS services(like s3) to connect to your code. 5. AWS Services used here are - Amazon Q ,Amazon CodeWhisperer, Amazon CodeCatalyst ,AWS Chatbot. Its already in place and these things can be done in seconds ... If it really becomes a large thing then you can imagine its results. Please ignore any mistake in grammar. #aws #awscloud #awscommunity #amazon #amazonq

Hey there, I wrote a small tutorial about how to work with AWS Textract to analyze and extract text from images with Node.js.