AWS SageMaker Tutorials
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Updated
Jun 25, 2020 - Jupyter Notebook
AWS SageMaker Tutorials
Code and files for deploying a plagiarism detector using AWS SageMaker
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.
Making a Sentiment Analisys Prediction using AWS SageMaker ML/AI package models.
Repository for submission to the AWS Marketplace Developer Challenge
The project deals with solving the global pandemic using Cloud-based services. The facility will be provided to the patient at the comfort of their home. The idea is to provide appointment booking, DNA sample collection, and a report delivery mechanism to the end-user.
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.
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
using deep learning model in production environment 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'.
Udacity Project: Build an end-to-end plagiarism classification model. Apply skills to clean data, extract meaningful features, and deploy a plagiarism classifier in SageMaker.
The project is sample of use SageMaker component to train and deploy ML models for visual attribution in AWS infrastructure.
Playing Flappy Bird based on Deep Q Network (DQN) and Dueling DQN
Code and associated files created or filled in, used in my learning during the Machine Learning Engineer Udacity Nanodegree Program
We have created a machine learning classification model on AWS-SageMaker to predict whether an email is a spam or not. Also have implemented MLOps operations using AWS-CloudFormation and Codepipeline on the entire system architecture.
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