Industries updates

Open foundation models (FMs) have become a cornerstone of generative AI innovation, enabling organizations to build and customize AI applications while maintaining control over their costs and deployment strategies. By providing high-quality, openly available models, the AI community fosters rapid iteration, knowledge sharing, and cost-effective solutions that benefit both developers and end-users. DeepSeek AI, a …

Deploy DeepSeek-R1 Distilled Llama models in Amazon Bedrock Read More »

Generative AI can revolutionize organizations by enabling the creation of innovative applications that offer enhanced customer and employee experiences. Intelligent document processing, translation and summarization, flexible and insightful responses for customer support agents, personalized marketing content, and image and code generation are a few use cases using generative AI that organizations are rolling out in …

Generative AI operating models in enterprise organizations with Amazon Bedrock Read More »

DeepSeek R1 is now available in the model catalog on Azure AI Foundry and GitHub, joining a diverse portfolio of over 1,800 models, including frontier, open-source, industry-specific, and task-based AI models. As part of Azure AI Foundry, DeepSeek R1 is accessible on a trusted, scalable, and enterprise-ready platform, enabling businesses to seamlessly integrate advanced AI …

DeepSeek R1 is now available on Azure AI Foundry and GitHub Read More »

Kent Walker shares three national security imperatives for the AI era.

Generative AI and large language models (LLMs) are revolutionizing organizations across diverse sectors to enhance customer experience, which traditionally would take years to make progress. Every organization has data stored in data stores, either on premises or in cloud providers. You can embrace generative AI and enhance customer experience by converting your existing data into …

Develop a RAG-based application using Amazon Aurora with Amazon Kendra Read More »

In production generative AI applications, responsiveness is just as important as the intelligence behind the model. Whether it’s customer service teams handling time-sensitive inquiries or developers needing instant code suggestions, every second of delay, known as latency, can have a significant impact. As businesses increasingly use large language models (LLMs) for these critical tasks and …

Optimizing AI responsiveness: A practical guide to Amazon Bedrock latency-optimized inference Read More »

Evaluating large language models (LLMs) is crucial as LLM-based systems become increasingly powerful and relevant in our society. Rigorous testing allows us to understand an LLM’s capabilities, limitations, and potential biases, and provide actionable feedback to identify and mitigate risk. Furthermore, evaluation processes are important not only for LLMs, but are becoming essential for assessing …

Track LLM model evaluation using Amazon SageMaker managed MLflow and FMEval Read More »

The AI platform shift brings immense opportunity, but the road to success isn’t always easy or clear. That’s why we’ve asked customers, partners, Microsoft experts, and other leaders to share their journeys and insights in our new podcast, Leading the Shift. In each episode, we’ll explore how our guests are using data, AI, and cloud …

Introducing Leading the Shift, a new Microsoft Azure podcast Read More »

Learn how Google teams built the Pixel 9 series’ Add Me feature, which uses AI for easier group photos.

Amazon SageMaker provides a seamless experience for building, training, and deploying machine learning (ML) models at scale. Although SageMaker offers a wide range of built-in algorithms and pre-trained models through Amazon SageMaker JumpStart, there are scenarios where you might need to bring your own custom model or use specific software dependencies not available in SageMaker …

Create a SageMaker inference endpoint with custom model & extended container Read More »