What is Amazon SageMaker? - Amazon SageMaker
Amazon SageMaker pricingAre you a first-time user of Amazon SageMaker?

What is Amazon SageMaker?

Amazon SageMaker is a fully managed machine learning (ML) service. With SageMaker, data scientists and developers can quickly and confidently build, train, and deploy ML models into a production-ready hosted environment. It provides a UI experience for running ML workflows that makes SageMaker ML tools available across multiple integrated development environments (IDEs). You can store and share your data without having to build and manage your own servers. This frees you up to quickly get you or your organizations started to collaboratively build and develop your ML workflow. The provided SageMaker managed ML algorithms are optimized to run efficiently against extremely large data in a distributed environment. With native support for bring-your-own-algorithms and frameworks, SageMaker offers flexible distributed training options that adjust to your specific workflows. Deploy a model into a secure and scalable environment by launching with a few clicks from the SageMaker console.

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Amazon SageMaker pricing

Amazon SageMaker is free to try if your uses are within the AWS Free Tier or if you are using Amazon SageMaker Studio Lab. As with other AWS products, there are no contracts or minimum commitments for using SageMaker. Training and hosting are billed by minutes of usage, with no minimum fees and no upfront commitments. For more information about SageMaker Free Tier limits and the cost of using SageMaker, see Amazon SageMaker Pricing.

Are you a first-time user of Amazon SageMaker?

If you are a first-time user of SageMaker, we recommend that you go through the following:

  1. Overview of machine learning with Amazon SageMaker - Get an overview of the machine learning (ML) lifecycle and learn of some of SageMaker’s solutions. This page explains key concepts and describes the core components involved in building AI solutions with SageMaker.

  2. Get started - If you’re interested in using or just trying out SageMaker, quickly and confidently get set up with SageMaker using a guide based on how you intend to use the service.

  3. Use automated ML, no-code, or low-code - Learn about low to no code ML options that simplifies your ML workflow by automating machine learning tasks. If you are new to SageMaker, it provides the easiest learning path. It also serves as an excellent ML learning tool that provides visibility into the code with notebooks generated for each of the automated ML tasks.

  4. Use machine learning environments offered by SageMaker - Familiarize yourself with the ML environments you can use to develop your ML workflow that includes information and examples on using ready-to-use and custom models.

  5. Explore other topics – Explore what SageMaker has to offer in table of contents on the left panel. With the ML lifecycle in mind, you can navigate to the lifecycle stages and learn about the solutions offered by SageMaker.

  6. Amazon SageMaker resources - Take note of the list of SageMaker developer resources.