Software Development Exam  >  Software Development Notes  >  Introduction to Amazon Web Services(AWS)  >  Amazon Web Services - Machine Learning

Amazon Web Services - Machine Learning | Introduction to Amazon Web Services(AWS) - Software Development PDF Download

Amazon Machine Learning is a service that allows to develop predictive applications by using algorithms, mathematical models based on the user’s data.

Amazon Machine Learning reads data through Amazon S3, Redshift and RDS, then visualizes the data through the AWS Management Console and the Amazon Machine Learning API. This data can be imported or exported to other AWS services via S3 buckets.

It uses “industry-standard logistic regression” algorithm to generate models.

Types of Tasks Performed by Amazon Machine Learning

Three different types of tasks can be performed by Amazon Machine learning service −

  • A binary classification model can predict one of the two possible results, i.e. either yes or no.

  • A multi-class classification model can predict multiple conditions. For example, it can track a customer's online orders.

  • A regression model results in an exact value. Regression models can predict the best-selling price for a product or the number of units that will sell.

How to Use Amazon Machine Learning?

Step 1 − Sign in to AWS account and select Machine Learning. Click the Get Started button.

Amazon Machine Learning

Step 2 − Select Standard Setup and then click Launch.

Launch

Step 3 − In the Input data section, fill the required details and select the choice for data storage, either S3 or Redshift. Click the Verify button.

Input Data

Step 4 − After S3 location verification is completed, Schema section opens. Fill the fields as per requirement and proceed to the next step.

Schema

Step 5 − In Target section, reselect the variables selected in Schema section and proceed to the next step.

Target

Step 6 − Leave the values as default in Row ID section and proceed to the Review section. Verify the details and click the Continue button.

Following are some screenshots of Machine Learning services.

Data Set Created by Machine Learning

Data sources


Summary Made by Machine Learning

ML Models


Exploring Performance Using Machine Learning

ML Model Report

Features of Amazon Machine Learning

Easy to create machine learning models − It is easy to create ML models from data stored in Amazon S3, Amazon Redshift, Amazon RDS and query these models for predictions by using Amazon ML APIs and wizards.

High performance − Amazon ML prediction APIs can be used further to generate billions of predictions for the applications. We can use them within interactive web, mobile, or desktop applications.

Cost-efficient − Pay only for what we use without any setup charges and no upfront commitments.

The document Amazon Web Services - Machine Learning | Introduction to Amazon Web Services(AWS) - Software Development is a part of the Software Development Course Introduction to Amazon Web Services(AWS).
All you need of Software Development at this link: Software Development
24 docs

Top Courses for Software Development

24 docs
Download as PDF
Explore Courses for Software Development exam

Top Courses for Software Development

Signup for Free!
Signup to see your scores go up within 7 days! Learn & Practice with 1000+ FREE Notes, Videos & Tests.
10M+ students study on EduRev
Related Searches

past year papers

,

Sample Paper

,

Amazon Web Services - Machine Learning | Introduction to Amazon Web Services(AWS) - Software Development

,

Viva Questions

,

Free

,

Semester Notes

,

Exam

,

ppt

,

Summary

,

shortcuts and tricks

,

mock tests for examination

,

study material

,

Important questions

,

Objective type Questions

,

Amazon Web Services - Machine Learning | Introduction to Amazon Web Services(AWS) - Software Development

,

pdf

,

MCQs

,

Extra Questions

,

Amazon Web Services - Machine Learning | Introduction to Amazon Web Services(AWS) - Software Development

,

Previous Year Questions with Solutions

,

practice quizzes

,

video lectures

;