Deploy and Operationalize a ML Solution Using Amazon SageMaker on AWS
In this lab, you will build and deploy a machine learning model using Python3 on Amazon SageMaker.
Terms and conditions apply.
Challenge
Create a SageMaker notebook instance
In this challenge, you will create the notebook instance that you'll use to download and process your data. During the process, you'll need to create a new IAM role that allows SageMaker to access the data held within S3.
Challenge
Prepare the data
In this challenge, we will use the Amazon SageMaker notebook instance to preprocess the data that you need to train your machine learning model and then upload the data to Amazon S3.
Challenge
Train the model to learn from the data
In this challenge, you will use your training dataset to train your machine learning model.
Challenge
Deploy model and evaluate performance
In this challenge, you deploy the trained model to an EC2 endpoint and evaluate the performance and accuracy of the machine learning model.
Provided environment for hands-on practice
We will provide the credentials and environment necessary for you to practice right within your browser.
Guided walkthrough
Follow along with the author’s guided walkthrough and build something new in your provided environment!
Did you know?
On average, you retain 75% more of your learning if you get time for practice.
Recommended prerequisites
- Familiarity with machine learning concepts
- Basic understanding of cloud services
- Fundamental understanding of Python3