Use LLMs for building real-world apps

Amazon SageMaker Llama 2 Inference via Response Streaming

Deploy Audiocraft Musicgen on Amazon SageMaker using SageMaker Endpoints for Async Inference.

This repository contains samples for fine-tuning embedding models using Amazon SageMaker. Embedding models are useful for tasks such as semantic similarity, text clustering, and information retrieval. Fine-tuning these models on your specific domain data can greatly improve their performance.

This GitHub repository showcases the implementation of a comprehensive end-to-end MLOps pipeline using Amazon SageMaker pipelines to deploy and manage 100x machine learning models. The pipeline covers data pre-processing, model training/re-training, hyperparameter tuning, data quality check,model quality check, model registry, and model deployment.

It shows how to use langchain for sagemaker endpoint.

This code is used for making MindsDB run on Amazon SageMaker.

Deploy Audiocraft Musicgen on Amazon SageMaker using SageMaker Endpoints for Async Inference.

This is a template for deploying a FastAPI endpoint on AWS SageMaker.

The objective of this project is to forecast weekly retail store sales based on historical data using XGBoost Sagemaker

AWS CDK resource that handles the deployment and update of SageMaker models' endpoint any time the code changes in your S3 bucket.

Object detection flask application using OpenCV DNN with support for various model formats.

Sagemaker endpoint deployment with Lambda and API Gateway