/Operationalizing-an-AWS-ML-Project

This project is a part of the assessment in the Udacity's AWS Machine Learning Engineer Nanodegree Program.

Primary LanguageJupyter Notebook

Operationalizing an AWS ML Project

Dog Image Classification

The objective of this project is to finish the following steps:

  1. Train and deploy a model on Sagemaker, using the most appropriate instances. Set up multi-instance training in your Sagemaker notebook.
  2. Adjust your Sagemaker notebooks to perform training and deployment on EC2.
  3. Set up a Lambda function for your deployed model. Set up auto-scaling for your deployed endpoint as well as concurrency for your Lambda function.
  4. Ensure that the security on your ML pipeline is set up properly. Step 1: Training and deployment on Sagemaker: Created Sagemaker notebook instance I have used ml.t3.large as this is sufficient and has a low cost to run my notebook.