Category: Learning Levels

In recent years, the rapid advancement of artificial intelligence and machine learning (AI/ML) technologies has revolutionized various aspects of digital content creation. One particularly exciting development is the emergence of video generation capabilities, which offer unprecedented opportunities for companies across diverse industries. This technology allows for the creation of short video clips that can be […]

In this post, we discuss how SageMaker HyperPod and SageMaker Studio can improve and speed up the development experience of data scientists by using IDEs and tooling of SageMaker Studio and the scalability and resiliency of SageMaker HyperPod with Amazon EKS. The solution simplifies the setup for the system administrator of the centralized system by using the governance and security capabilities offered by the AWS services.

In this post, we present a benchmark of different understanding models from the Amazon Nova family available on Amazon Bedrock, to provide insights on how you can choose the best model for a meeting summarization task.

This post provides instructions to configure a structured data retrieval solution, with practical code examples and templates. It covers implementation samples and additional considerations, empowering you to quickly build and scale your conversational data interfaces.

In the context of distributed systems and microservices architecture, orchestrating communication between diverse components presents significant challenges. However, with the launch of Amazon Bedrock Agents, the landscape is evolving, offering a simplified approach to agent creation and seamless integration of the return of control capability. In this post, we explore how Amazon Bedrock Agents revolutionizes agent creation and demonstrates the efficacy of the return of control capability in orchestrating complex interactions between multiple systems.

In this post, we show you how to build generative AI applications on Amazon Web Services (AWS) using the capabilities of Amazon Bedrock, highlighting how Amazon Bedrock can be used at each step of your generative AI journey. This guide is valuable for both experienced AI engineers and newcomers to the generative AI space, helping you use Amazon Bedrock to its fullest potential.

In this post, we show how Netsertive introduced a generative AI-powered assistant into MLX, using Amazon Bedrock and Amazon Nova, to bring their next generation of the platform to life.

The Institute of Science Tokyo has successfully trained Llama 3.3 Swallow, a 70-billion-parameter large language model (LLM) with enhanced Japanese capabilities, using Amazon SageMaker HyperPod. The model demonstrates superior performance in Japanese language tasks, outperforming GPT-4o-mini and other leading models. This technical report details the training infrastructure, optimizations, and best practices developed during the project.

Gardenia Technologies, a data analytics company, partnered with the AWS Prototyping and Cloud Engineering (PACE) team to develop Report GenAI, a fully automated ESG reporting solution powered by the latest generative AI models on Amazon Bedrock. This post dives deep into the technology behind an agentic search solution using tooling with Retrieval Augmented Generation (RAG) and text-to-SQL capabilities to help customers reduce ESG reporting time by up to 75%. We demonstrate how AWS serverless technology, combined with agents in Amazon Bedrock, are used to build scalable and highly flexible agent-based document assistant applications.

In this post, we demonstrate how to use Amazon Bedrock and LangGraph to build a personalized customer support experience for an ecommerce retailer. By integrating the Mistral Large 2 and Pixtral Large models, we guide you through automating key customer support workflows such as ticket categorization, order details extraction, damage assessment, and generating contextual responses.