Category: Advanced (300)

In this post, we show how the SageMaker Core SDK simplifies the developer experience while providing API for seamlessly executing various steps in a general ML lifecycle. We also discuss the main benefits of using this SDK along with sharing relevant resources to learn more about this SDK.

In this post, we demonstrate how HAQM Bedrock Guardrails can improve the robustness of the agent framework. We are able to stop our chatbot from responding to non-relevant queries and protect personal information from our customers, ultimately improving the robustness of our agentic implementation with HAQM Bedrock Agents.

In this post, we present a solution that harnesses the power of generative AI to streamline the user onboarding process for financial services through a digital assistant.

In this post, we presented how to create a fully serverless voice-based contextual chatbot using HAQM Bedrock with Anthropic Claude.

In this post, we explain how to use Anthropic Claude on HAQM Bedrock to generate synthetic data for evaluating your RAG system.

In this post, we analyze strategies for governing access to HAQM Bedrock and SageMaker JumpStart models from within SageMaker Canvas using AWS Identity and Access Management (IAM) policies. You’ll learn how to create granular permissions to control the invocation of ready-to-use HAQM Bedrock models and prevent the provisioning of SageMaker endpoints with specified SageMaker JumpStart models.

In this blog post, we explore how you can use HAQM Q Business to build generative AI assistants that enhance employee experience and boost productivity. HAQM Q Business seamlessly integrates with internal data sources, knowledge bases, and productivity tools to equip your workforce with instant access to information, automated tasks, and personalized support.

In this post, AWS collaborates with Meta’s PyTorch team to showcase how you can use PyTorch’s torchtune library to fine-tune Meta Llama-like architectures while using a fully-managed environment provided by HAQM SageMaker Training.

In this post, we walk through how AWS can help accelerate a brand’s creative efforts with access to a powerful image-to-image model from Stable Diffusion available on HAQM Bedrock to interactively create and edit art and logo images.

In this post, we demonstrate a solution using HAQM FSx for NetApp ONTAP with HAQM Bedrock to provide a RAG experience for your generative AI applications on AWS by bringing company-specific, unstructured user file data to HAQM Bedrock in a straightforward, fast, and secure way.