https://github.com/awslabs/gluonts
Probabilistic time series modeling in Python
https://github.com/awslabs/generative-ai-cdk-constructs
AWS Generative AI CDK Constructs are sample implementations of AWS CDK for common generative AI patterns.
https://github.com/awslabs/sagemaker-debugger
Amazon SageMaker Debugger provides functionality to save tensors during training of machine learning jobs and analyze those tensors
https://github.com/awslabs/renate
Library for automatic retraining and continual learning
https://github.com/altescy/metamaker
⚗️ Simple command line tool to train and deploy your machine learning models with AWS SageMaker
https://github.com/deepset-ai/haystack-sagemaker
🚀 This repo is a showcase of how you can use models deployed on AWS SageMaker in your Haystack Retrieval Augmented Generative AI pipelines
https://github.com/awslabs/realtime-fraud-detection-with-gnn-on-dgl
An end-to-end blueprint architecture for real-time fraud detection(leveraging graph database Amazon Neptune) using Amazon SageMaker and Deep Graph Library (DGL) to construct a heterogeneous graph from tabular data and train a Graph Neural Network(GNN) model to detect fraudulent transactions in the IEEE-CIS dataset.
https://github.com/awslabs/sagemaker-defect-detection
Detect Defects in Products from their Images using Amazon SageMaker
ml_with_aws_sagemaker
Learn how to scale up ML/AI pipelines using AWS SageMaker (GPUs, Cloud computing)
foundation-model-benchmarking-tool
Foundation model benchmarking tool. Run any model on any AWS platform and benchmark for performance across instance type and serving stack options.
https://github.com/awslabs/aiops-modules
AIOps modules is a collection of reusable Infrastructure as Code (IaC) modules for Machine Learning (ML), Foundation Models (FM), Large Language Models (LLM) and GenAI development and operations on AWS
https://github.com/awslabs/datawig-sagemaker
Wraps https://github.com/awslabs/datawig into a SageMaker Docker container.