Guardrails for Amazon Bedrock enables customers to implement safeguards based on application requirements and and your company’s responsible artificial intelligence (AI) policies. It can help prevent undesirable content, block prompt attacks (prompt injection and jailbreaks), and remove sensitive information for privacy. You can combine multiple policy types to configure these safeguards for different scenarios and apply them across foundation models (FMs) on Amazon Bedrock, as well as custom and third-party FMs outside of Amazon Bedrock. Guardrails can also be integrated with Agents for Amazon Bedrock and Knowledge Bases for Amazon Bedrock. Guardrails for Amazon Bedrock provides additional customizable safeguards on top of native protections offered by FMs, delivering safety features that are among the best in the industry: Blocks as…
Knowledge Bases for Amazon Bedrock now supports additional data connectors (in preview)
Using Knowledge Bases for Amazon Bedrock, foundation models (FMs) and agents can retrieve contextual information from your company’s private data sources for Retrieval Augmented Generation (RAG). RAG helps FMs deliver more relevant, accurate, and customized responses. Over the past months, we’ve continuously added choices of embedding models, vector stores, and FMs to Knowledge Bases. Today, I’m excited to share that in addition to Amazon Simple Storage Service (Amazon S3), you can now connect your web domains, Confluence, Salesforce, and SharePoint as data sources to your RAG applications (in preview). New data source connectors for web domains, Confluence, Salesforce, and SharePoint By including your web domains, you can give your RAG applications access to your public data, such as your company’s…
Introducing Amazon Q Developer in SageMaker Studio to streamline ML workflows
Today, we are announcing a new capability in Amazon SageMaker Studio that simplifies and accelerates the machine learning (ML) development lifecycle. Amazon Q Developer in SageMaker Studio is a generative AI-powered assistant built natively into the SageMaker JupyterLab experience. This assistant takes your natural language inputs and crafts a tailored execution plan for your ML development lifecycle by recommending the best tools for each task, providing step-by-step guidance, generating code to get started, and offering troubleshooting assistance when you encounter errors. It also helps when facing challenges such as translating complex ML problems into smaller tasks and searching for relevant information in the documentation. You may be a first-time user who evaluates Amazon SagaMaker for generative artificial intelligence (generative AI)…
Monitor data events in Amazon S3 Express One Zone with AWS CloudTrail
In a News Blog post for re:Invent 2023, we introduced you to Amazon S3 Express One Zone, a high-performance, single-Availability Zone (AZ) storage class purpose-built to deliver consistent single-digit millisecond data access for your most frequently accessed data and latency-sensitive applications. It is well-suited for demanding applications and is designed to deliver up to 10x better performance than S3 Standard. S3 Express One Zone uses S3 directory buckets to store objects in a single AZ. Starting today, S3 Express One Zone supports AWS CloudTrail data event logging, allowing you to monitor all object-level operations like PutObject, GetObject, and DeleteObject, in addition to bucket-level actions like CreateBucket and DeleteBucket that were already supported. This enables auditing for governance and compliance, and can…