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1. Create your fashion assistant application using Amazon Titan models and Amazon Bedrock Agents

   In this post, we implement a fashion assistant agent using Amazon Bedrock Agents and the Amazon Titan family models. The fashion assistant provides a personalized, multimodal conversational experience.

2. How Aviva built a scalable, secure, and reliable MLOps platform using Amazon SageMaker

   In this post, we describe how Aviva built a fully serverless MLOps platform based on the AWS Enterprise MLOps Framework and Amazon SageMaker to integrate DevOps best practices into the ML lifecycle. This solution establishes MLOps practices to standardize model development, streamline ML model deployment, and provide consistent monitoring.

3. Visier’s data science team boosts their model output 10 times by migrating to Amazon SageMaker

   In this post, we learn how Visier was able to boost their model output by 10 times, accelerate innovation cycles, and unlock new opportunities using Amazon SageMaker.

4. Implement model-independent safety measures with Amazon Bedrock Guardrails

   In this post, we discuss how you can use the ApplyGuardrail API in common generative AI architectures such as third-party or self-hosted large language models (LLMs), or in a self-managed Retrieval Augmented Generation (RAG) architecture.

5. How Schneider Electric uses Amazon Bedrock to identify high-potential business opportunities

   In this post, we show how the team at Schneider collaborated with the AWS Generative AI Innovation Center (GenAIIC) to build a generative AI solution on Amazon Bedrock to solve this problem. The solution processes and evaluates each requests for proposal (RFP) and then routes high-value RFPs to the microgrid subject matter expert (SME) for approval and recommendation.

6. Achieve operational excellence with well-architected generative AI solutions using Amazon Bedrock

   In this post, we discuss scaling up generative AI for different lines of businesses (LOBs) and address the challenges that come around legal, compliance, operational complexities, data privacy and security.

7. Elevate workforce productivity through seamless personalization in Amazon Q Business

   In this post, we explore how Amazon Q Business uses personalization to improve the relevance of responses and how you can align your use cases and end-user data to take full advantage of this capability

8. Best practices for building robust generative AI applications with Amazon Bedrock Agents – Part 1

   In this post, we show you how to create accurate and reliable agents. Agents helps you accelerate generative AI application development by orchestrating multistep tasks. Agents use the reasoning capability of foundation models (FMs) to break down user-requested tasks into multiple steps.

9. AWS recognized as a first-time Leader in the 2024 Gartner Magic Quadrant for Data Science and Machine Learning Platforms

   AWS has been recognized as a Leader in the 2024 Gartner Magic Quadrant for Data Science and Machine Learning Platforms. The post highlights how AWS's continued innovations in services like Amazon Bedrock and Amazon SageMaker have enabled organizations to unlock the transformative potential of generative AI.

10. Build a serverless voice-based contextual chatbot for people with disabilities using Amazon Bedrock

    In this post, we presented how to create a fully serverless voice-based contextual chatbot using Amazon Bedrock with Anthropic Claude.