sagemaker groundtruth examples

Making a Sentiment Analisys Prediction using AWS SageMaker ML/AI package models.

It contains the reward function and model generated that used in battle-of-schools March 2020 at RIT

Repository for submission to the AWS Marketplace Developer Challenge

AWS SageMaker Tutorials

Code and files for deploying a plagiarism detector using AWS SageMaker

Completed dialogue summarization task using Generative AI. Utilized FLAN-T5 Large Language Model using Hugging Face Transformers and experimented with zero-shot, one-shot, and few-shot inference. Performed PEFT fine-tuning to improve inferences and used ROUGE metrics for evaluation and reinforcement learning to generate more positive summaries.

Solution for Kaggle competition "Bike Sharing Demand". The solution using AutoGluon's Tabular Predictor provides a good overview of which model to choose as the base model for this problem.

Distributed Deep Reinforcement Learning in an HPC system and deployment to the Cloud

This repository contains my Rock Paper Scissor Classification App Project. It has been programmed using Kotlin and it uses AWS Sagemaker as a backend service to perform inference.

A deployed RNN performing sentiment analysis on movie reviews complete with publicly accessible API and a simple web page which interacts with the deployed endpoint.

An end-to-end plagiarism classification model deployed in AWS SageMaker.

A model that predicts an individual's insurance charges.

Capstone Project for the AWS Machine Learning Engineer Nanodegree.

In this project I created an Aurora PostgreSQL database instance, integrating with Amazon Comprehend for sentiment analysis, and analyzing sentiments of records in the database table.

AWS Sagemaker Ground Truth Scripts for Human Evaluation of Dialog Response Generation Models | Supports Amazon Mechanical Turk (MTurk) / Private in-lab study backend

Machine Learning Deployment using AWS SageMaker

Code and files associated with the Project called 'Plagiarism Detector', part of the Machine Learning Engineer Nanodegree at Udacity, School of AI. This project is the third of this Nanodegree, and is part of the module 'Machine Learning, Case Studies'.