Foundation model benchmarking tool. Run any model on Amazon SageMaker and benchmark for performance across instance type and serving stack options.
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Updated
Jun 10, 2024 - Jupyter Notebook
Foundation model benchmarking tool. Run any model on Amazon SageMaker and benchmark for performance across instance type and serving stack options.
A collection of localized (Korean) AWS AI/ML workshop materials for hands-on labs.
Terraform code, aws scripts and pipeline templates for the AWS-IaC-mlops-pipeline.
Write local debuggable Python which traverses your powerful remote infra. Deploy as-is. Unobtrusive, unopinionated, PyTorch-like APIs.
AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet.
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.
A library for training and deploying machine learning models on Amazon SageMaker
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
This repo provides sample generative AI stacks built atop the AWS Generative AI CDK Constructs.
AI book for everyone
AWS Generative AI CDK Constructs are sample implementations of AWS CDK for common generative AI patterns.
Know How Guide and Hands on Guide for AWS
A modular and comprehensive solution to deploy a Multi-LLM and Multi-RAG powered chatbot (Amazon Bedrock, Anthropic, HuggingFace, OpenAI, Meta, AI21, Cohere) using AWS CDK on AWS
A helper library to connect into Amazon SageMaker with AWS Systems Manager and SSH (Secure Shell)
An Exasol extension to interact with AWS SageMaker from inside the database
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