Detect Defects in Products from their Images using Amazon SageMaker

MLOps on AWS using Amazon SageMaker Pipelines

Fast model deployment on AWS Sagemaker

This project promulgates an automated end-to-end ML pipeline that trains a biLSTM network for sentiment analysis, experiment tracking, benchmarking by model testing and evaluation, model transitioning to production followed by deployment into cloud instance via CI/CD

Simple guide to use tf.estimator and deploy to AWS SageMaker (after training with your GPU)

Train Multilabel NLP Classification using Pytroch and huggingface with deployment using Amazon SageMaker

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.

The repository for NLP projects like Sentiment Analysis, Part of Speech, Topic Modeling, Machine Translation, Chatbox, DNN speech Recognizer etc.

SageMaker Domain (SageMaker Classic) Disaster Recovery

Movie review sentiment analysis application trained and deployed on AWS. End-to-end development and deployment process using AWS Sagemaker, Lambda, API Gateway

Python CDK stack for network infrastructure for SageMaker VPC only mode.

Elastic Data Factory

The repository is to predict human joint location from JPG images that have a pixel size of 256*256. This will be done using the Movnet Singlepose thunder pretrained model, which will be deployed on AWS using CF and SDK.

This repo automates training of a Deep-AR algorithm using a Sagemaker pipeline.

A repo for setting pipeline for deploying model as sagemaker endpoint

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