Category: Learning Levels

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.

Amazon SageMaker Ground Truth is a powerful data labeling service offered by AWS that provides a comprehensive and scalable platform for labeling various types of data, including text, images, videos, and 3D point clouds, using a diverse workforce of human annotators. In addition to traditional custom-tailored deep learning models, SageMaker Ground Truth also supports generative […]

In this post, we demonstrate how Amazon 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 Amazon Bedrock Agents.

In this post, we will demonstrate how to set up Slack connector for Amazon Q Business to sync communications from both public and private channels, reflective of user permissions.

In this post, we collaborate with the team working on PyTorch at Meta to showcase how the torchtitan library accelerates and simplifies the pre-training of Meta Llama 3-like model architectures. We showcase the key features and capabilities of torchtitan such as FSDP2, torch.compile integration, and FP8 support that optimize the training efficiency.

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 describe the development of a generative AI Slack application powered by Amazon Bedrock and Amazon Kendra. This is designed to be an internal-facing Slack chat assistant that helps answer questions related to the indexed content.

In this post, we discuss how you can use the ApplyGuardrail API in common generative AI architectures such as third-party or self-hosted large language models (LLMs), or in a self-managed Retrieval Augmented Generation (RAG) architecture.

In this post, we discuss scaling up generative AI for different lines of businesses (LOBs) and address the challenges that come around legal, compliance, operational complexities, data privacy and security.

In this post, we explore how Amazon Q Business uses personalization to improve the relevance of responses and how you can align your use cases and end-user data to take full advantage of this capability