Introduction to Amazon Machine Learning
SPL-35 Version 2.1.1
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Amazon Machine Learning (Amazon ML) is a robust machine learning platform that allows software developers to train predictive models and use them to create powerful predictive applications. Amazon ML allows business domain experts and software developers to focus on the problems they are trying to solve using predictive models rather than running and maintaining the compute and storage infrastructure required to train a supervised machine learning model.
In this lab, you will build out a simple Amazon Machine Learning model that will be used to predict whether or not a customer would like a restaurant. The results of the prediction will be based on a datasource of customer reviews. The information passed to Amazon Machine Learning will be the age, gender and budget preference of the prospective customer as well as the price and cuisine type of the restaurant. The real-time predictions endpoint will then return one of the following predictions:
- very good
This will represent the likelihood that the customer will enjoy the restaurant.
By the end of this lab, you will be able to:
- Download a data set used to train a model in the Amazon Machine Learning service
- Examine and understand the attributes used to train the model
- Train and evaluate the model using Amazon Machine Learning
- Make real-time predictions using the model
Technical Knowledge Prerequisites
To successfully complete this lab, you should:
- Be familiar with the Amazon S3 service
- Understand the concepts of bucket and object
- Understand how to perform put and get operations on objects in an S3 bucket using the S3 Management Console or AWS CLI
- Have taken the "Introduction to Amazon Simple Storage Service (S3)" lab
- Have an understanding of the IAM policy language and how to use bucket policies to provide secure access to your S3 bucket from the machine learning service
Other AWS Services
Other AWS Services than the ones needed for this lab are disabled by IAM policy during your access time in this lab. In addition, the capabilities of the services used in this lab are limited to what is required by the lab and in some cases are even further limited as an intentional aspect of the lab design. Expect errors when accessing other services or performing actions beyond those provided in this lab guide.
What is Amazon Machine Learning?
Amazon ML can be used to make predictions for a variety of purposes. For example, you could build a model in Amazon ML that will predict whether a given customer is likely to respond to a marketing offer. Amazon ML creates models from supervised data sets. This means that the model is based on a set of previous observations. This set of observations consists of features or attributes as well as the target outcome. In the marketing offer example, the features might include the age, profession, and gender of the customer. The target outcome (also called the target variable) would be whether that particular customer responded to the marketing offer or not.
The process of creating a model from a set of known observations is called training. Once you have trained a model in Amazon ML, you can then use the model to predict outcomes from a set of attributes that matches the attributes used to train the model. Amazon ML scales so that you can make thousands of predictions concurrently. This is important, as today machine learning is often used to provide predictions in near real-time. In this lab, you will be using a machine learning model to predict which restaurants a customer is likely to favor based on the results of a search query.
Notice the lab properties below the lab title:
- setup - The estimated time to set up the lab environment
- access - The time the lab will run before automatically shutting down
- completion - The estimated time the lab should take to complete
- At the top of your screen, launch your lab by clicking
If you are prompted for a token, use the one distributed to you (or credits you have purchased).
A status bar shows the progress of the lab environment creation process. The AWS Management Console is accessible during lab resource creation, but your AWS resources may not be fully available until the process is complete.
- Open your lab by clicking
This will automatically log you into the AWS Management Console.
Please do not change the Region unless instructed.
Common login errors
Error : Federated login credentials
If you see this message:
- Close the browser tab to return to your initial lab window
- Wait a few seconds
- Click again
You should now be able to access the AWS Management Console.
Error: You must first log out
If you see the message, You must first log out before logging into a different AWS account:
- Click click here
- Close your browser tab to return to your initial Qwiklabs window
- Click again
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