Category: *Post Types

In this post, you will learn how to secure reserved GPU capacity for short-term workloads using Amazon Elastic Compute Cloud (Amazon EC2) Capacity Blocks for ML and Amazon SageMaker training plans. These solutions can address GPU availability challenges when you need short-term capacity for load testing, model validation, time-bound workshops, or preparing inference capacity ahead of a release.

In this post, you will learn how to implement reinforcement learning with verifiable rewards (RLVR) to introduce verification and transparency into reward signals to improve training performance. This approach works best when outputs can be objectively verified for correctness, such as in mathematical reasoning, code generation, or symbolic manipulation tasks. You will also learn how to layer techniques like Group Relative Policy Optimization (GRPO) and few-shot examples to further improve results. You’ll use the GSM8K dataset (Grade School Math 8K: a collection of grade school math problems) to improve math problem solving accuracy, but the techniques used here can be adapted to a wide variety of other use cases.

Today, we’re announcing a preview of Amazon Bedrock AgentCore Payments, a new set of features in Amazon Bedrock AgentCore that enables AI agents to instantly access and pay for what they use. AgentCore Payments was developed in partnership with Coinbase and Stripe.

Tomofun, the Taiwan-headquartered pet-tech startup behind the Furbo Pet Camera, is redefining how pet owners interact with their pets remotely. To reduce costs and maintain accuracy, Tomofun turned to EC2 Inf2 instances powered by AWS Inferentia2, the Amazon purpose-built AI chips. In this post, we walk through the following sections in detail.

Hapag-Lloyd’s Digital Customer Experience and Engineering team, distributed between Hamburg and Gdańsk, drives digital innovation by developing and maintaining customer-facing web and mobile products. In this post, we walk you through our generative AI–powered feedback analysis solution built using Amazon Bedrock, Elasticsearch, and open-source frameworks like LangChain and LangGraph

Today, we’re excited to announce that Amazon SageMaker AI MLflow Apps now support MLflow version 3.10, bringing enhanced capabilities for generative AI development and streamlined experiment tracking to your generative AI workflows. Building on the foundations established with Amazon SageMaker AI MLflow Apps, this latest version introduces powerful new features for observability, evaluation, and generative […]

In this post, you will learn how you can use Amazon Nova Foundation Models in Amazon Bedrock to apply generative AI techniques for both business protection and enhancement. You can identify obvious and disguised attempts at direct contact while gaining valuable insights into customer sentiment and service improvement opportunities.

Business leaders across industries rely on operational dashboards as the shared source of truth that their teams execute against daily. But dashboards are built to answer known questions. When teams need to explore further, ad-hoc, multi-dimensional, or unforeseen questions, they hit a bottleneck. They wait hours or days for BI teams to build new views […]

Generate recommendations from production traces, validate them with batch evaluation and A/B testing, and ship with confidence. AI agents that perform well at launch don’t stay that way. As models evolve, user behavior shifts, and prompts get reused in new contexts they were never designed for. Agent quality quietly degrades. In most teams, the improvement […]

Amazon SageMaker AI now offers an agentic experience that changes this. Developers describe their use case using natural language, and the AI coding agent streamlines the entire journey, from use case definition and data preparation through technique selection, evaluation, and deployment. In this post, we walk you through the model customization lifecycle using SageMaker AI agent skills.