1. Multi-Turn Reinforcement Learning on Amazon SageMaker AI: A hands-on guide Most fine-tuning teaches a model to produce a better answer to a single prompt. Agentic tasks are different. An agent that files an expense report or triages a support ticket takes 10/24 01:48 ♡
2. Orchestrating SageMaker AI endpoint auto scaling with AWS CDK Updating the Amazon Sagemaker AI real-time endpoint configuration (changing the instance type, instance count, or environment variables) causes deployments through AWS CloudFormati 10/24 00:30 ♡
3. Visualize points and routes with GeoJSON in Amazon Quick Sight layer maps With Amazon Quick Sight layer maps, you can now plot points and routes, like store locations across a city or flights leaving an airport. Geospatial maps help businesses spot patte 10/24 00:24 ♡
4. How Bayer Crop Science built an agentic decision engine on Amazon Bedrock AgentCore with AWS Unified Operations Plant breeding decisions depend on data that spans genomics, field trials, imagery, environmental conditions, and model outputs. Bayer Crop Science runs hundreds of machine learnin 10/24 00:21 ♡
5. Amazon Polly delivers a major upgrade to its Generative engine Amazon Polly Generative engine now offers more natural and accurate speech output. We’re introducing an upgrade to the Amazon Polly Generative engine that delivers more expressive, 10/23 02:06 ♡
6. How IskayPet unified corporate knowledge with Amazon Bedrock Managed Knowledge Bases IskayPet’s office teams had a knowledge challenge. Answers to everyday operational questions, such as a return policy, a veterinary protocol, or an onboarding step, lived in hundre 10/23 02:01 ♡
7. Announcing new ways to customize models with SageMaker AI, without managing GPUs Companies getting real results from AI aren’t only using foundation models (FMs) off the shelf. They’re customizing open-weight models using post-training techniques such as reinfo 10/23 02:00 ♡
8. Write your own training loop — without managing GPUs — with Amazon SageMaker training session APIs Fine-tuning a large language model (LLM) today usually forces a tradeoff between training loop control and operational simplicity. With fully managed services you select a base mod 10/23 02:00 ♡
9. Deploy AI in a HIPAA-eligible environment with Amazon Quick Healthcare organizations want the same thing from AI that every other business wants: faster answers, less manual work, and insights that reach decision-makers in time to act on th 10/23 01:48 ♡
10. Stream low-latency speech from AI agents using Kokoro TTS on Amazon SageMaker AI A customer asks an AI voice agent a question and waits. The agent already has an answer, it’s midway through generating a long, multi-paragraph response, but the customer hears not 10/23 01:45 ♡
