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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 »

Amazon has introduced two new creative content generation models on Amazon Bedrock: Amazon Nova Canvas for image generation and Amazon Nova Reel for video creation. These models transform text and image inputs into custom visuals, opening up creative opportunities for both professional and personal projects. Nova Canvas, a state-of-the-art image generation model, creates professional-grade images …

Image and video prompt engineering for Amazon Nova Canvas and Amazon Nova Reel Read More »

Amazon Bedrock has emerged as the preferred choice for tens of thousands of customers seeking to build their generative AI strategy. It offers a straightforward, fast, and secure way to develop advanced generative AI applications and experiences to drive innovation. With the comprehensive capabilities of Amazon Bedrock, you have access to a diverse range of …

Security best practices to consider while fine-tuning models in Amazon Bedrock Read More »

A common use case with generative AI that we usually see customers evaluate for a production use case is a generative AI-powered assistant. However, before it can be deployed, there is the typical production readiness assessment that includes concerns such as understanding the security posture, monitoring and logging, cost tracking, resilience, and more. The highest …

Secure a generative AI assistant with OWASP Top 10 mitigation Read More »

This post highlights startups in the Google for Startups Growth Academy: AI for Education program, showcasing how artificial intelligence is transforming education.

Attaching a custom Docker image to an Amazon SageMaker Studio domain involves several steps. First, you need to build and push the image to Amazon Elastic Container Registry (Amazon ECR). You also need to make sure that the Amazon SageMaker domain execution role has the necessary permissions to pull the image from Amazon ECR. After …

Streamline custom environment provisioning for Amazon SageMaker Studio: An automated CI/CD pipeline approach Read More »

The rise of AI has opened new avenues for enhancing customer experiences across multiple channels. Technologies like natural language understanding (NLU) are employed to discern customer intents, facilitating efficient self-service actions. Automatic speech recognition (ASR) translates spoken words into text, enabling seamless voice interactions. With Amazon Lex bots, businesses can use conversational AI to integrate …

Enhance your customer’s omnichannel experience with Amazon Bedrock and Amazon Lex Read More »