What Is Amazon SageMaker? (How It Works, Security + More)

Amazon is so much more than just a place to shop online, as the No. 2 Fortune 500 company continues to grow and expand with new technology and digital services.

However, if you’re intrigued by Amazon’s tech, then you may be wondering: what is Amazon SageMaker? I was fascinated by this machine learning program, so I put together this review of everything I know about SageMaker on Amazon Web Services!

What Is Amazon SageMaker In 2024?

Amazon SageMaker is an innovative program on Amazon Web Services offering developers the chance to create and use cloud machine learning models in 2024. Since the initial launch in November 2017, SageMaker has provided fully managed tools and workflows to assist with product and tech development. Engineers, data scientists, and business analysts are the top users on SageMaker.

If you want to know more about Amazon SageMaker, including what it does and where it’s available, check out the rest of this detailed guide!

What Does Amazon SageMaker Do?

Amazon SageMaker supports developers as they build, train, and implement machine learning models for a wide range of applications, such as edge devices and embedded systems.

Machine learning models are files trained to identify patterns through an algorithm, which is used to manage and ultimately learn from the data as part of product development, project management, and many other areas of business.

SageMaker is backed by fully managed infrastructure, and serves as a valuable resource for engineers, scientists, strategists, and analysts who can use machine learning models to guide projects to successful completion.

Data scientists use Amazon SageMaker for 24/7 cloud learning and modeling, which is incredibly reliable, because there’s no scheduled maintenance or downtime.

Instead, SageMaker APIs run in Amazon data centers, known for their high availability and replication ability in case of a zone outage or server failure.

For this reason, SageMaker gives developers a highly secure cloud machine learning program.

This is where workflows and models are developed and deployed to streamline operations and make better use of data.

What Is Amazon SageMaker Security?

One of the main concerns for developers interested in a cloud machine learning platform is security, and whether the program can offer the high level of security required for sensitive data and workflows.

Luckily, for those on Amazon SageMaker, the platform is protected by stringent AWS security standards, starting with identity and access management authentication.

Amazon SageMaker security measures also include groups and encryption for storage volumes, protecting the privacy and integrity of machine learning models at every stage.

Machine learning system artifacts are encrypted during transfer and storage, with API and console requests made over a high-security SSL connection.

To set access permissions for training or deployment, you must pass identity checks.

Also, for extra security, you can also use encrypted data storage buckets, the same as the Amazon S3 cloud storage solution.

Other options for SageMaker encrypted security include KMS keys for notebooks, training assignments, and endpoints, as well as AWS PrivateLink support and Amazon Virtual Privacy Cloud (VPC).

How Do You Pay For Amazon SageMaker?

How Do You Pay For Amazon SageMaker?

Not only is Amazon SageMaker highly secure, but it also has flexible pricing, as you’re only charged for what you actually need and use.

Amazon SageMaker charges for machine learning computing, data processing, and storage resources.

This means you pay SageMaker for hosting each notebook, training the machine learning model, making predictions, and logging outputs.

The good thing is you can choose how many and what type of hosted notebooks you want to use, as well as the model hosting and training.

SageMaker users don’t have to commit to anything upfront, and there are no minimum fees, so you can just use the program when you need it.

Additionally, you can try SageMaker for free as part of the AWS Free Tier, which begins from the first month after creating a SageMaker resource.

You can visit the official Amazon SageMaker pricing page for more details on how much it will cost for your organization’s needs.

As well, there’s also a pricing calculator, so you can estimate how much it will be based on your machine learning model needs.

Also, there are a few things you can do to keep costs down, such as avoiding unnecessary charges from idle resources.

By optimizing SageMaker resources through configuration and programmatic solutions, you can prevent wasted resources and have lower costs overall.

Plus, even though SageMaker has end-to-end workflows, you can integrate existing tools to save on costs, as it’s easy to transfer results from each stage in and out of the SageMaker cloud as required.

Who Can Use Amazon SageMaker?

Amazon SageMaker is a cutting-edge cloud learning resource for developers and data scientists at all levels, whether they are entry-level engineers or senior managers.

While Amazon is based in the U.S., and has thousands of American businesses on Amazon Web Services, SageMaker and similar solutions are available around the globe.

If your organization is enrolled in Amazon Web Services, then SageMaker should be available to you, so long as you’re in a supported region.

The AWS global infrastructure is highlighted in this AWS region table.

This outlines the availability of Amazon SageMaker in North and South America, as well as Europe and Asia Pacific.

If you want to get started with Amazon SageMaker in a supported region, you can log into AWS, or visit aws.amazon.com/sagemaker/ for sign-up details.

To learn more, you can also read our posts on what is Amazon Luna, is Amazon better than eBay, and Amazon competitors.


More and more developers are signing up for Amazon SageMaker, a cloud machine learning program offered by Amazon Web Services since 2017.

With fully managed tools, support, and security, SageMaker presents an innovative opportunity for business analysts, data strategists, and developers to use computer science and artificial intelligence to create data and algorithms for a wide variety of projects.

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Marques Thomas

Marques Thomas graduated with a MBA in 2011. Since then, Marques has worked in the retail and consumer service industry as a manager, advisor, and marketer. Marques is also the head writer and founder of QuerySprout.com.

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