Artificial Intelligence and Machine Learning

In this post, we share how Apollo Tyres used generative AI with HAQM Bedrock to harness the insights from their machine data in a natural language interaction mode to gain a comprehensive view of its manufacturing processes, enabling data-driven decision-making and optimizing operational efficiency.

In this post, we demonstrate how organizations can enhance their incident management capabilities by integrating PagerDuty Advance, an innovative set of agentic and generative AI capabilities that automate response workflows and provide real-time insights into operational health, with HAQM Q Business. We show how to configure PagerDuty Advance as a data accessor for HAQM Q indexes, so you can search and access enterprise knowledge across multiple systems during incident response.

In the context of distributed systems and microservices architecture, orchestrating communication between diverse components presents significant challenges. However, with the launch of HAQM 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 HAQM Bedrock Agents revolutionizes agent creation and demonstrates the efficacy of the return of control capability in orchestrating complex interactions between multiple systems.

You can now import custom weights for Qwen2, Qwen2_VL, and Qwen2_5_VL architectures, including models like Qwen 2, 2.5 Coder, Qwen 2.5 VL, and QwQ 32B. In this post, we cover how to deploy Qwen 2.5 models with HAQM Bedrock Custom Model Import, making them accessible to organizations looking to use state-of-the-art AI capabilities within the AWS infrastructure at an effective cost.

In this post, we show you how to build generative AI applications on HAQM Web Services (AWS) using the capabilities of HAQM Bedrock, highlighting how HAQM 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 HAQM Bedrock to its fullest potential.

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

In this post, we demonstrate how you can use services like HAQM Nova, HAQM Rekognition, and HAQM Polly to automate the creation of accessible audio descriptions for video content. This approach can significantly reduce the time and cost required to make videos accessible for visually impaired audiences.

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 HAQM 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.

Learn how Articul8 is redefining enterprise generative AI with domain-specific models that outperform general-purpose LLMs in real-world applications. In our latest blog post, we dive into how HAQM SageMaker HyperPod accelerated the development of Articul8’s industry-leading semiconductor model—achieving 2X higher accuracy that top open source models while slashing deployment time by 4X.

In this post, we illustrate how VideoAmp, a media measurement company, worked with the AWS Generative AI Innovation Center (GenAIIC) team to develop a prototype of the VideoAmp Natural Language (NL) Analytics Chatbot to uncover meaningful insights at scale within media analytics data using HAQM Bedrock.