Author name: auto feeder

Today, we are announcing the general availability of vector search for Amazon MemoryDB, a new capability that you can use to store, index, retrieve, and search vectors to develop real-time machine learning (ML) and generative artificial intelligence (generative AI) applications with in-memory performance and multi-AZ durability. With this launch, Amazon MemoryDB delivers the fastest vector …

Vector search for Amazon MemoryDB is now generally available Read More »

Organizations often struggle to solve their business problems in areas like claims processing, inventory tracking, and project approvals. Custom business applications could provide a solution to solve these problems and help an organization work more effectively but have historically required a professional development team to build and maintain. But often, development capacity is unavailable or …

Build enterprise-grade applications with natural language using AWS App Studio (preview) Read More »

When we launched Amazon Q Business in April 2024, we also previewed Amazon Q Apps. Amazon Q Apps is a capability within Amazon Q Business for users to create generative artificial intelligence (generative AI)–powered apps based on the organization’s data. Users can build apps using natural language and securely publish them to the organization’s app …

Amazon Q Apps, now generally available, enables users to build their own generative AI apps Read More »

Today, we’re making the Amazon Q Developer (in your IDE) customization capability generally available for inline code completion, and we’re launching a preview of customization for the chat. You can now customize Amazon Q to generate specific code recommendations from private code repositories in the IDE code editor and in the chat. Amazon Q Developer …

Customize Amazon Q Developer (in your IDE) with your private code base Read More »

With Agents for Amazon Bedrock, generative artificial intelligence (AI) applications can run multistep tasks across different systems and data sources. A couple of months back, we simplified the creation and configuration of agents. Today, we are introducing in preview two new fully managed capabilities: Retain memory across multiple interactions – Agents can now retain a …

Agents for Amazon Bedrock now support memory retention and code interpretation (preview) Read More »

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 …

Guardrails for Amazon Bedrock can now detect hallucinations and safeguard apps built using custom or third-party FMs Read More »

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, …

Knowledge Bases for Amazon Bedrock now supports additional data connectors (in preview) Read More »

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 …

Introducing Amazon Q Developer in SageMaker Studio to streamline ML workflows Read More »