Solutions for Artificial Intelligence

The easiest way to build and scale generative AI applications with foundation models

Build, train, and deploy ML models—including FMs—for any use case with fully managed infrastructure, tools, and workflows

The MLOps Workload Orchestrator solution helps you streamline and enforce architecture best practices for machine learning (ML) model productionization.

This Guidance shows how you can optimize your generative AI models using Amazon SageMaker, a service where you can build, train, and deploy large language models (LLMs) at scale.

This Guidance demonstrates how to build an internal Software-as-a-Service (SaaS) platform that provides access to foundation models, like those available through Amazon Bedrock, to different business units or teams within your organization.

This Guidance demonstrates how to deploy a machine learning inference architecture on Amazon Elastic Kubernetes Service (Amazon EKS).

This Guidance demonstrates how to deploy a generative artificial intelligence (AI) model provided by Amazon SageMaker JumpStart to create an asynchronous SageMaker endpoint with the ease of the AWS Cloud Development Kit (AWS CDK).

This Guidance demonstrates how to build an application enabling users to ask questions directly of relational databases using natural language queries (NLQ).

This Guidance demonstrates how to process remote sensing imagery using machine learning models that automatically detect and identify objects collected from satellites, unmanned aerial vehicles, and other remote sensing devices.