The newest recipients of Google.org’s AI Opportunity Fund
Google.org has announced which organizations will receive the final $10 million in funding from its $75 million AI Opportunity Fund to provide American nonprofits with A…
Amazon Bedrock Guardrails image content filters provide industry-leading safeguards, helping customer block up to 88% of harmful multimodal content: Generally available today
Amazon Bedrock Guardrails announces the general availability of image content filters, enabling you to moderate both image and text content in your generative AI applications. Previously limited to text-only filtering, this enhancement now provides comprehensive content moderation across both modalities. This new capability removes the heavy lifting required to build your own image safeguards or …
5 new ways we’re helping students and young people learn and grow using AI
These efforts, including new responsible AI curriculum and a $1 million grant to MIT RAISE, all aim to help students and educators use AI safely and effectively.
Integrating custom dependencies in Amazon SageMaker Canvas workflows
When implementing machine learning (ML) workflows in Amazon SageMaker Canvas, organizations might need to consider external dependencies required for their specific use cases. Although SageMaker Canvas provides powerful no-code and low-code capabilities for rapid experimentation, some projects might require specialized dependencies and libraries that aren’t included by default in SageMaker Canvas. This post provides an …
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Generate training data and cost-effectively train categorical models with Amazon Bedrock
In this post, we explore how you can use Amazon Bedrock to generate high-quality categorical ground truth data, which is crucial for training machine learning (ML) models in a cost-sensitive environment. Generative AI solutions can play an invaluable role during the model development phase by simplifying training and test data creation for multiclass classification supervised …
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Enable Amazon Bedrock cross-Region inference in multi-account environments
Amazon Bedrock cross-Region inference capability that provides organizations with flexibility to access foundation models (FMs) across AWS Regions while maintaining optimal performance and availability. However, some enterprises implement strict Regional access controls through service control policies (SCPs) or AWS Control Tower to adhere to compliance requirements, inadvertently blocking cross-Region inference functionality in Amazon Bedrock. This …
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Amazon SageMaker JumpStart adds fine-tuning support for models in a private model hub
Amazon SageMaker JumpStart is a machine learning (ML) hub that provides pre-trained models, solution templates, and algorithms to help developers quickly get started with machine learning. Within SageMaker JumpStart, the private model hub feature allows organizations to create their own internal repository of ML models, enabling teams to share and manage models securely within their …
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Generative AI-powered game design: Accelerating early development with Stability AI models on Amazon Bedrock
In the competitive world of game development, staying ahead of technological advancements is crucial. Generative AI has emerged as a game changer, offering unprecedented opportunities for game designers to push boundaries and create immersive virtual worlds. At the forefront of this revolution is Stability AI’s cutting-edge text-to-image AI model, Stable Diffusion 3.5 Large (SD3.5 Large), …
Amazon Bedrock launches Session Management APIs for generative AI applications (Preview)
Amazon Bedrock announces the preview launch of Session Management APIs, a new capability that enables developers to simplify state and context management for generative AI applications built with popular open source frameworks such as LangGraph and LlamaIndex. Session Management APIs provide an out-of-the-box solution that enables developers to securely manage state and conversation context across …
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Enhance deployment guardrails with inference component rolling updates for Amazon SageMaker AI inference
Deploying models efficiently, reliably, and cost-effectively is a critical challenge for organizations of all sizes. As organizations increasingly deploy foundation models (FMs) and other machine learning (ML) models to production, they face challenges related to resource utilization, cost-efficiency, and maintaining high availability during updates. Amazon SageMaker AI introduced inference component functionality that can help organizations …