Imagine you are building a website for recommendations. In your website, users see personalized movie title recommendations delivered in real time.
As part of your site, you want to generate movie title recommendations for users of the website. These movie title recommendations should be based on the users' browsing and viewing history.
In this lab, you learn how to use Amazon Personalize to train a solution for movie title recommendations. You use the AWS SDK for Python to prepare the data; then, you create a solution and campaign, and deploy the recommendation model in Amazon Personalize.
To make recommendations, Amazon Personalize uses a machine learning model that is trained with your data. The data used to train the model is stored in related datasets in a dataset group. Each model is trained by using a recipe that contains an algorithm for a specific use case. In Amazon Personalize, a trained model is known as a solution version. A solution version is deployed for use in a campaign. Users of your applications can receive recommendations through the campaign. For example, a campaign can show movie recommendations on a website or application where the title shown is based on viewing habits that were part of the dataset.
In Module 1, you create an Amazon SageMaker notebook instance and attach the appropriate policies required in this lab for your SageMaker role. Finally, you create the Jupyter notebook that you use during the lab.
Time to Complete Module: 20 Minutes
In this module, you learned about the example Amazon Personalize model you train in this lab. You also set up an AWS account and your lab environment with an Amazon SageMaker Notebook instance, IAM role, and a Jupyter notebook.
You are now ready to start the lab. In the next module, you download and prepare your dataset.