Ecosystem & Stack: aws

An open-source infrastructure providing secure, isolated cloud sandboxes for executing AI-generated code with developer-friendly SDKs.

OSWorld is a comprehensive benchmarking platform designed to evaluate multimodal AI agents on open-ended tasks within real computer environments.

A model-driven Python SDK for rapidly building and deploying AI agents, from simple assistants to complex autonomous workflows, with minimal code.

A unified system to run, manage, and scale AI workloads efficiently across any infrastructure, including Kubernetes, Slurm, and over 20 cloud providers, optimizing cost and resource availability.

A collection of Jupyter notebooks and a new Python SDK demonstrating how to build, train, and deploy machine learning models on Amazon SageMaker.

An open-source feature store that streamlines the management and serving of features for AI/ML models, ensuring consistency between training and inference.

Learn to design, train, and deploy a real-time financial advisor LLM system using a hands-on, three-pipeline approach.

A general-purpose workflow orchestrator providing a fully managed workflow-as-a-service for data and ML pipelines at scale.

A comprehensive practical guide and accompanying codebase for building, deploying, and monitoring advanced LLM and RAG applications on AWS, emphasizing LLMOps best practices.

An open-source framework that organizes AI prompts by real-world tasks to solve AI integration challenges and augment human capabilities.

An all-in-one API platform providing advanced search, recommendations, and Retrieval-Augmented Generation (RAG) capabilities for developers.

A free, hands-on course teaching how to build and deploy production-ready LLM and RAG systems, including a personalized 'LLM Twin', using LLMOps best practices.

An open-source, free, self-hosted alternative to Cypress Dashboard, enabling unlimited parallel test execution and comprehensive test result management.