AIML

Starter kits are complete, deployable solutions that address common, repeatable business problems. They deploy the services that make up a solution according to best practices, helping you optimize costs and become familiar with these kinds of architectural patterns without a large investment in training. In this post, we showcase a starter kit for Amazon Q Business. If you have a repository of documents that you need to turn into a knowledge base quickly, or simply want to test out the capabilities of Amazon Q Business without a large investment of time at the console, then this solution is for you.

Aetion is a leading provider of decision-grade real-world evidence software to biopharma, payors, and regulatory agencies. In this post, we review how Aetion is using Amazon Bedrock to help streamline the analytical process toward producing decision-grade real-world evidence and enable users without data science expertise to interact with complex real-world datasets.

In this post, we review how Aetion’s Smart Subgroups Interpreter enables users to interact with Smart Subgroups using natural language queries. Powered by Amazon Bedrock and Anthropic’s Claude 3 large language models (LLMs), the interpreter responds to user questions expressed in conversational language about patient subgroups and provides insights to generate further hypotheses and evidence.

In this blog post, we provide an introduction to preparing your own dataset for LLM training. Whether your goal is to fine-tune a pre-trained model for a specific task or to continue pre-training for domain-specific applications, having a well-curated dataset is crucial for achieving optimal performance.

This post provides step-by-step instructions for creating a collaborative multi-agent framework with reasoning capabilities to decouple business applications from FMs. It demonstrates how to combine Amazon Bedrock Agents with open source multi-agent frameworks, enabling collaborations and reasoning among agents to dynamically execute various tasks. The exercise will guide you through the process of building a reasoning orchestration system using Amazon Bedrock, Amazon Bedrock Knowledge Bases, Amazon Bedrock Agents, and FMs. We also explore the integration of Amazon Bedrock Agents with open source orchestration frameworks LangGraph and CrewAI for dispatching and reasoning.

Docker Desktop 4.37 streamlines AI-driven development with the new AI Catalog integration, command-line management capabilities, upgraded components, and enhanced stability to empower modern developers.

In this post, we’ll demonstrate how to configure an Amazon Q Business application and add a custom plugin that gives users the ability to use a natural language interface provided by Amazon Q Business to query real-time data and take actions in ServiceNow.

We look at Docker’s 2024 milestones and innovations in security, AI, and more, as well as how we helped teams build, test, and deploy more easily and quickly than ever.

We show how to create Git agent and use this Git agent to understand PR branches for a variety of user personas — without needing to know the ins and outs of Git.

Exploring AI-assisted tools for UI writers, we demonstrate how to enhance GitHub PR review workflows to identify user-facing text changes, and offer a step-by-step guide and insights into leveraging LLMs effectively.

We explore the interaction of AI agents and editors by mixing tool definitions with prompts using a simple Markdown-based canvas.

Docker's AI Catalog simplifies AI development by offering a curated platform that helps developers and publishers easily integrate AI capabilities into applications.

Learn how to turn a PDF into a bunch of images with the help of Docker and AI tools.

Learn how to map your codebase in order to provide context for creating better AI-generated code fixes.

IBM's Granite AI models, optimized for business applications, are now available on Docker Hub, making it easier for developers to deploy, scale, and customize AI-powered apps.

In this Docker Labs GenAI series installment, learn how to use an AI assistant to script a tool based on a specific definition.

Explore how to use Docker and LLMs to streamline workflows for command-line tools to enhance the process of reading docs, troubleshooting errors, and running commands.

Dive into the “Secure by Design” approach essential for AI systems, focusing on embedding security features from the start to fortify AI against emerging threats.

Explore the Labs AI Tools for Devs extension in Docker Desktop, designed for prompt authors to easily build, run, and share their AI tools. Get started quickly with our guide to installing the extension and running your first prompts.

Discover Docker Desktop 4.34's enhancements that boost security, scalability, and productivity for developers. This release includes a readily available MSI installer for simpler Windows deployment, improved authentication processes, smart storage compaction for WSL2, and seamless NVIDIA AI Workbench integration for streamlined model training and local development.