Amazon SageMaker Projects empower data scientists to self-serve Amazon Web Services (AWS) tooling and infrastructure to organize all entities of the machine learning (ML) lifecycle, and further enable organizations to standardize and constrain the resources available to their data science...

Generative AI applications seem simple—invoke a foundation model (FM) with the right context to generate a response. In reality, it’s a much more complex system involving workflows that invoke FMs, tools, and APIs and that use domain-specific data to...

In the landscape of generative AI, organizations are increasingly adopting a structured approach to deploy their AI applications, mirroring traditional software development practices. This approach typically involves separate development and production environments, each with its own AWS account, to...

When ingesting data into Amazon OpenSearch, customers often need to augment data before putting it into their indexes. For instance, you might be ingesting log files with an IP address and want to get a geographic location for the...

Emerging transformer-based vision models for geospatial data—also called geospatial foundation models (GeoFMs)—offer a new and powerful technology for mapping the earth’s surface at a continental scale, providing stakeholders with the tooling to detect and monitor surface-level ecosystem conditions such...

Agentic Retrieval Augmented Generation (RAG) applications represent an advanced approach in AI that integrates foundation models (FMs) with external knowledge retrieval and autonomous agent capabilities. These systems dynamically access and process information, break down complex tasks, use external tools,...