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There’s still a common misconception that machine learning requires extensive resources and is reserved for only the most mathematically oriented data scientists. However, this myth is slowly dying as more people gain awareness of cloud infrastructure services like AWS (Amazon Web Services), which provide the necessary tools to make machine learning more accessible to every business and individual.

Are you struggling to acquire computational power for your prediction generator? Making limited progress with machine learning because you can’t get your hands on that powerful GPU card? Read on to find out whether AWS Machine Learning could have a solution for you.

What Is Machine Learning?

Machine learning (ML) is a form of sorting, creating or monitoring data without human interference. Its purpose is to locate and apply patterns to improve and evolve systems through large amounts of data. Concretely, ML may be employed to create recommendations for an audience, detect fraudulent activity or provide automated customer support.

This is all achieved through complex algorithms that create predictions through the previously mentioned patterns. However, creating algorithms is not easy and requires extensive knowledge and resources. AWS Machine Learning eliminates this dilemma.

So What Is AWS Machine Learning?

Put simply, AWS Machine Learning allows you to use machine learning regardless of your skillset, time and available resources. You too can create, use and manage ML in your applications. AWS makes machine learning services accessible to businesses and individuals who otherwise lack huge hardware budgets. 

You can choose between different types of services, and there are three notable categories: AI Services, Machine Learning Services and Frameworks.

Amazon AI Services

If you’re someone who prefers ready-made solutions, you can use an Amazon AI Service like CodeGuru that provides recommendations for optimizing your code. CodeGuru uses machine learning to detect any bugs and issues that affect overall code quality.

Some other tools to note include Amazon Kendra, an intelligent search service, and Amazon Connect, an intelligent virtual contact center that provides real-time analysis. 

The AI service category is a great option for users who lack the ability or desire to train models or understand the technicalities behind machine learning. These services are based on AWS’s other products and provide ready-made solutions.

Machine Learning Services

For developers who have some understanding behind how the ML process works and wish to train machines with their own data, AWS offers their own Machine Learning Services. These allow you to save time and ease the overall complexity of using machine learning.

SageMaker is one of the more notable tools from AWS’s suite of Machine Learning Services. It has built-in tools for every step of the development of your service. Using the graphic interface provided, you can rapidly learn to use machine learning in your applications. It guides you through the entirety of your workflow from data labeling to deployment. We’ll go over SageMaker in greater detail below.


AWS also provides support for many major frameworks. Python and machine learning often go hand in hand. This is why AWS allows for the use of PyTorch, an open source framework that can be managed using SageMaker. With SageMaker you can train PyTorch models to create estimators.

Although frameworks can be configured within SageMaker, these services are typically reserved for professional developers or academics.

Now that you know about the different categories of machine learning services AWS provides to its users, you might be wondering: Why should you use AWS?

Reasons to Use AWS Machine Learning

Each service type comes with its own positives and negatives. However, AWS attempts to combat several issues with using machine learning in applications. This allows you to keep your costs low, as you pay for what you use.

Additionally, you’re free to choose what you need based on your skillset, budget and goals. This means that any business or individual — irrespective of resources — can incorporate AWS services for their specific needs.

Luckily, there are numerous AWS courses widely available to answer your simplest questions and satisfy your deepest curiosities. These of course provide only the foundations you need to get started with machine learning and are just the initial step of becoming a machine learning engineer.

Of course, the main benefit of AWS Machine Learning is the ability to gain access to computational power without needing expensive hardware. Depending on your chosen category, AWS allows you to save time in three ways: 

  • using pre-existing AI services, where there’s no need to build anything; 
  • using SageMaker, which allows you to avoid spending time on configuring your machine learning toolchain;
  • using libraries and integrated tools on AWS to avoid worrying about infrastructure.

These options each allow you to quickly move to the development part of your application, allowing you to spend less time over-optimizing algorithms before you’ve pinpointed the value of your machine learning project.

However, it goes without saying that no service is flawless for every given case.

Downsides of AWS Machine Learning

While the costs of using AWS Machine Learning services may be scalable and generally affordable, certain aspects can make them extremely costly. For example, if instead of generating your data and keeping it within the confines of AWS you choose to export it, costs may end up mounting.

Because of certain functions or decisions being limited by financial repercussions, some larger enterprises opt for setting up their own services using open-source software. To be clear, open-source software also requires effort and resources to set up, but on a large scale (as with enterprise environments), the savings of doing so within your organization can be considerable.

Nonetheless, you’re always welcome to remain within the confines of AWS to accelerate the processes you’re incorporating. However, while this may allow for the whole operation to be cost-effective, you may face limitations in the form of pre-structured projects. This poses an issue where your requirements for tools go beyond their capabilities or the available infrastructure.

For example, you could encounter technical limitations on the resources available within your geographical area. You might also frequently require technical support. In the latter case, you would need additional packages, which can be costly.

Of course, you can request to remove or change these limitations. However, it’s important to evaluate whether AWS’s solutions are right for your project.

If you’ve considered all pros and cons and want to give SageMaker a try, here’s how you can do it.

Using Amazon SageMaker

SageMaker is one of the most popular developer tools designed to simplify the process of setting up a machine learning service. You can stick to its simplified graphical interface or go deeper and incorporate. Let’s touch on some of the options and capabilities that SageMaker offers as a tool.

To start using SageMaker, you don’t need to have undertaken extensive AWS developer training, but instead can learn as you go and use your newfound knowledge from AWS courses in practice.

The process begins by labeling data for your machine to study. For example, if your goal is to create a plant identification app, this process would consist of going through images of plants and defining them in the UI by dragging and dropping. Doing this manually is possible, but of course can consume a lot of time. You can subcontract this data labeling to other humans straight through AWS, or make use of premade datasets.

Using the labeled data, the algorithm then needs to be trained using either your own or the SageMaker’s algorithms. This is where patterns are found and used to later create predictions.

Finally, after some fine-tuning and sub-steps, the algorithm is ready for deployment to AWS. Check out this detailed guide on starting with SageMaker by Amazon.

Keep Learning

Now that you have a general idea of how AWS Machine Learning tools work, as well as their benefits, drawbacks and potential, it’s time to dive deeper into your AWS cloud training.

Deeper knowledge and hands-on experience will allow you to freely use the various AWS tools to their full potential. If you’re searching for a path to becoming an AWS developer, look no further! Our expert-taught Machine Learning Engineer Nanodegree, developed in collaboration with AWS, immerses you in real-world projects using Amazon SageMaker. 

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