A library for training and deploying machine learning models on Amazon SageMaker
-
Updated
Jun 12, 2024 - Python
A library for training and deploying machine learning models on Amazon SageMaker
AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet.
A collection of localized (Korean) AWS AI/ML workshop materials for hands-on labs.
Probabilistic time series modeling in Python
This repo provides sample generative AI stacks built atop the AWS Generative AI CDK Constructs.
Foundation model benchmarking tool. Run any model on Amazon SageMaker and benchmark for performance across instance type and serving stack options.
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
AWS Generative AI CDK Constructs are sample implementations of AWS CDK for common generative AI patterns.
Write local debuggable Python which traverses your powerful remote infra. Deploy as-is. Unobtrusive, unopinionated, PyTorch-like APIs.
An Exasol extension to interact with AWS SageMaker from inside the database
A modular and comprehensive solution to deploy a Multi-LLM and Multi-RAG powered chatbot (Amazon Bedrock, Anthropic, HuggingFace, OpenAI, Meta, AI21, Cohere, Mistral) using AWS CDK on AWS
Deploy Audiocraft Musicgen on Amazon SageMaker using SageMaker Endpoints for Async Inference.
An end-to-end MLOps pipeline that reads data from PrestoDB to train an ML model and deploy on SageMaker for batch and realtime inference.
Terraform code, aws scripts and pipeline templates for the AWS-IaC-mlops-pipeline.
AI book for everyone
Know How Guide and Hands on Guide for AWS
Add a description, image, and links to the sagemaker topic page so that developers can more easily learn about it.
To associate your repository with the sagemaker topic, visit your repo's landing page and select "manage topics."