Skip to main content

What is Amazon Machine Learning? How to Get Started

The phrase machine learning seems to appear alongside every new technology or service. Despite its popularity, many people still don’t understand exactly what machine learning means, nor how to make practical use of it.

Today, we will explain the basics of machine learning, introduce you to Amazon Machine Learning, and show you how you can use it to improve your business through Amazon Web Services and their machine learning utility.

What is machine learning?

Machine learning is all around us. Believe it or not, each time we use our smartphones or computers, we’re using some form of machine learning. Of course, we’re not aware of the computing potential that is observing, tracking, and analyzing our online behavior on a daily basis. Servers all over the world are processing massive volumes of data using machine learning to find out what we’ll do next, where we’ll go, and what we’ll click on. The information that this data is revealing about us and our habits can be worth a fortune.

Machine learning is closely related to artificial intelligence, data mining, and deep learning, but it has a different focus. The main focus of machine learning is the development of computer programs that can automatically change when exposed to new data. defines machine learning as “a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed.” In other words, machine learning uses massive amounts of data to detect patterns, and when the repetition is recognized, computers can adjust the actions of their programs according to the newly identified behavior. To simplify this even further, we could say that it takes past patterns to predict future patterns or behaviors.

What is machine learning?

Machine learning was born out of pattern recognition and computational learning theory in artificial intelligence. It is also closely related to computational statistics, mathematical modeling, and composite algorithms.

Creating a model for pattern search requires developing a sophisticated mathematical model that will serve as the basis of the pattern. This task can be quite time-consuming and difficult for a small team that wants to exploit such a powerful resource.

Different mathematical models are the starting point for machine learning. Computers are using them to analyze data and learn from previous computations. The quality of the design and data portions enable a computer to produce reliable, repeatable decisions and results that you can use to improve your work.

The results of machine learning data analysis can help an organization identify profitable opportunities, avoid unknown risks, and make conclusions that can improve their decision-making processes.

Where are we already using machine learning?

Given the complexity and processing power needed to apply machine learning, you might think that only large corporations and institutions are using it. And you wouldn’t be far from the truth. However, thanks to automatic platforms such as Amazon Machine Learning, even small teams can get their hands on this exceptional technology. Let’s look at where we can find machine learning in the world around us.

Practical applications of machine learning technology are at work in some of the applications that we use on a daily basis.

How many times have you checked your Facebook feed today? The News Feed that you see there is generated by machine learning algorithms working in the background. They track your behavior and interactions with friends to find patterns and serve information that will be interesting to you. For example, you may have noticed that you see a lot of info in your feed about the people whose posts or photos you like or comment on, and less about the people with whom you rarely interact.

Where are we already using machine learning?

A similar example is Twitter’s “You might like,” “Who to follow,” and “While you were away” feeds. They are also making tailored suggestions based on your interests and activities while you like and share content on the platform.

Of course, it’s not just social networks. Netflix and Amazon Prime are also using machine learning to suggest what to watch next based on the movies, series, and programs that we have seen so far.

E-commerce websites such as and are also making suggestions for what to buy next by tracking our previous purchases. They are even sending us emails with good deals and discounts on items that we have checked out along the way, convincing us that this is the best deal for us.

Machine learning also has a significant role in other areas, namely fraud detection and online security. Machine learning is very efficient in detecting fraudulent behavior. Thanks to such actions, the internet is a much safer place today than it was only a decade ago.

It also has extensive application in government institutions, healthcare organizations, finance and banking, transportation, energy, and many other important areas of our lives.

How can you use machine learning for your project?

Machine learning is a powerful resource that can be applied in many different areas, from serving your customers with the most desirable content at any time, to forecasting stocks rated on the financial market. The question is, how you can use such a powerful resource for your projects without spending a fortune?

It’s understandable to think that you’ll need to invest a lot of time and money before you could be able to use machine learning in your projects. Just to get started you need to have:

  1. A super team of top mathematicians to develop a machine learning model and algorithms
  2. At least a dozen ninja developers who will implement this algorithm in your app/project
  3. A large set of quality data that you can use for analysis

When you sum it all up, it is quite an investment to have all of these resources at your disposal.

But what if there is a way to get the most of the necessary requirements without spending thousands of dollars before you even start your project? Enter the Amazon Machine Learning platform.

What is Amazon Machine Learning?

The Amazon Machine Learning service provides you with all of the equipment and tools you need to learn how to use machine learning. The service provides a rich toolset that will guide you through each step of the way, from creating machine learning models to data analysis, and applying results to your application. You will find all of the key concepts of Amazon Machine Learning here, as well as setup instructions, tutorials, training materials, data evaluation, analysis models, and many other useful resources.

For use cases and a real example for Python take a look at our post on Amazon Machine Learning.

What is Amazon Machine Learning?

How to use Amazon Machine Learning?

To be able to use the service, you’ll first need to create your machine learning model. The platform provides visualization tools and wizards that will help you start building your ML model without forcing you to learn all the complex algorithms. These tools will help you evaluate your models and fine-tune them if needed.

If you don’t need a custom model, you can use one of the predefined models for:

  • Fraud detection: Discover any attempts of abusive use of your app or website
  • Demand forecasting: Predict the need for your services or offerings
  • Predictive customer support: Make it easier to reply to the most common questions
  • Click predictions: See where your visitors will click next

Once you decide which model you will be using (custom or predefined), Amazon Machine Learning offers a simple API that you can implement in your app. You don’t need to develop a sophisticated infrastructure, or to write tons of code. All you have to do is apply the API, and works out of the box.

To be able to use your machine learning model on your current dataset, you will need to create a connection to read the data from Amazon S3, Amazon Redshift, or Amazon RDS (Relational Database Service). Please note: You will not be able to import data from elsewhere, so you need to use one of these three services to be able to apply your ML model.

The next step is to check the data quality. You can use Amazon visual tools to ensure that you have a quality data batch. The character of the information is quite relevant. If you have insufficient information, you can end up with bad predictions that will affect the overall quality of resulting actions. So, make sure that you have the best data set you can get.

Finally, when you start the entire process you can decide whether you want to have batch predictions for your entire data set at once or real-time data prediction. Amazon Machine Learning can provide you with both. It’s up to you to decide which best fits your requirements.

Where can I learn how to work with Amazon Machine Learning?

The official Amazon website has a wealth of material on machine learning, including handy examples, tutorials, and models that you can analyze to see some of the best practices.

If you need more practice than you can get by using the AWS website, we recommend the Cloud Academy Amazon Machine Learning Lab. It is specialized for human activity recognition, and it will give you some excellent examples and use cases for Amazon Machine Learning.

Where can I learn how to work with Amazon Machine Learning?

How much will it cost me?

We have some good news for you: The pricing for using Amazon Machine Learning is flexible. You will pay only for the resources you’re using and spending. There are no upfront costs and no massive investments on the compound infrastructure. Check the official AWS pricing page for more details.

With Amazon Machine Learning, the learning process will cost you far less, and the primary resource that you will have to invest is your time in getting you and your team up to speed.

Next steps: Getting started with machine learning

Machine learning, artificial intelligence, deep learning, and big data analysis are the key concepts that will shape our future and help us make quality decisions. One of the best ways to take advantage of the power of machine learning for your projects and your business is the Amazon Web Services platform.

Don’t forget to check out these Cloud Academy resources for learning more about Amazon Machine Learning:
Introduction to the Principles and Practice of Amazon Machine Learning

Course: Introduction to the Principles and Practice of Amazon Machine Learning
This in-depth introduction to the principles and practice of AML covers the basics of ML, working with data sources and how to manipulate data in Amazon Machine Learning to ensure a successful model, generating accurate predictions, and more.






Amazon Machine Learning for Human Activity Recognition

Hands-on Lab: Amazon Machine Learning for Human Activity Recognition
This lab will give you a general idea of how to use Amazon Machine Learning to build and use your own models. After a brief overview of the main machine learning concepts, we’ll use an open dataset from UCI to train and use a real-world model for HAR (Human Activity Recognition). We will walk through the whole process, from the dataset analysis and Datasource creation, all the way to model training/evaluation and a real Python script to generate real-time predictions.




Written by

Ivana Sabo

Ivana is Community Manager in Business Incubator Novi Sad by day and Content Writer by night. She is interested in startups, entrepreneurship, all things Cloud, internet marketing, and event organization. When she is not working Ivana enjoys adventurous life with her family.

Related Posts

— February 11, 2019

WaitCondition Controls the Pace of AWS CloudFormation Templates

AWS's WaitCondition can be used with CloudFormation templates to ensure required resources are running.As you may already be aware, AWS CloudFormation is used for infrastructure automation by allowing you to write JSON templates to automatically install, configure, and bootstrap your ...

Read more
  • AWS
— January 24, 2019

The 9 AWS Certifications: Which is Right for You and Your Team?

As companies increasingly shift workloads to the public cloud, cloud computing has moved from a nice-to-have to a core competency in the enterprise. This shift requires a new set of skills to design, deploy, and manage applications in the cloud.As the market leader and most mature p...

Read more
  • AWS
  • AWS certifications
— November 28, 2018

Two New EC2 Instance Types Announced at AWS re:Invent 2018 – Monday Night Live

The announcements at re:Invent just keep on coming! Let’s look at what benefits these two new EC2 instance types offer and how these two new instances could be of benefit to you. If you're not too familiar with Amazon EC2, you might want to familiarize yourself by creating your first Am...

Read more
  • AWS
  • EC2
  • re:Invent 2018
— November 21, 2018

Google Cloud Certification: Preparation and Prerequisites

Google Cloud Platform (GCP) has evolved from being a niche player to a serious competitor to Amazon Web Services and Microsoft Azure. In 2018, research firm Gartner placed Google in the Leaders quadrant in its Magic Quadrant for Cloud Infrastructure as a Service for the first time. In t...

Read more
  • AWS
  • Azure
  • Google Cloud
Khash Nakhostin
— November 13, 2018

Understanding AWS VPC Egress Filtering Methods

In order to understand AWS VPC egress filtering methods, you first need to understand that security on AWS is governed by a shared responsibility model where both vendor and subscriber have various operational responsibilities. AWS assumes responsibility for the underlying infrastructur...

Read more
  • Aviatrix
  • AWS
  • VPC
— November 10, 2018

S3 FTP: Build a Reliable and Inexpensive FTP Server Using Amazon’s S3

Is it possible to create an S3 FTP file backup/transfer solution, minimizing associated file storage and capacity planning administration headache?FTP (File Transfer Protocol) is a fast and convenient way to transfer large files over the Internet. You might, at some point, have conf...

Read more
  • Amazon S3
  • AWS
— October 18, 2018

Microservices Architecture: Advantages and Drawbacks

Microservices are a way of breaking large software projects into loosely coupled modules, which communicate with each other through simple Application Programming Interfaces (APIs).Microservices have become increasingly popular over the past few years. The modular architectural style,...

Read more
  • AWS
  • Microservices
— October 2, 2018

What Are Best Practices for Tagging AWS Resources?

There are many use cases for tags, but what are the best practices for tagging AWS resources? In order for your organization to effectively manage resources (and your monthly AWS bill), you need to implement and adopt a thoughtful tagging strategy that makes sense for your business. The...

Read more
  • AWS
  • cost optimization
— September 26, 2018

How to Optimize Amazon S3 Performance

Amazon S3 is the most common storage options for many organizations, being object storage it is used for a wide variety of data types, from the smallest objects to huge datasets. All in all, Amazon S3 is a great service to store a wide scope of data types in a highly available and resil...

Read more
  • Amazon S3
  • AWS
— September 18, 2018

How to Optimize Cloud Costs with Spot Instances: New on Cloud Academy

One of the main promises of cloud computing is access to nearly endless capacity. However, it doesn’t come cheap. With the introduction of Spot Instances for Amazon Web Services’ Elastic Compute Cloud (AWS EC2) in 2009, spot instances have been a way for major cloud providers to sell sp...

Read more
  • AWS
  • Azure
  • Google Cloud
  • SpotInst
— August 23, 2018

What are the Benefits of Machine Learning in the Cloud?

A Comparison of Machine Learning Services on AWS, Azure, and Google CloudArtificial intelligence and machine learning are steadily making their way into enterprise applications in areas such as customer support, fraud detection, and business intelligence. There is every reason to beli...

Read more
  • AWS
  • Azure
  • Google Cloud
  • Machine Learning
— August 17, 2018

How to Use AWS CLI

The AWS Command Line Interface (CLI) is for managing your AWS services from a terminal session on your own client, allowing you to control and configure multiple AWS services.So you’ve been using AWS for awhile and finally feel comfortable clicking your way through all the services....

Read more
  • AWS