Introducing Deep Learning with Amazon SageMaker

Deep learning (DL) is a fairly new but actively developing area of machine learning (ML). Over the past 15 years, DL has moved from research labs to our homes (such as smart homes and smart speakers) and cars (that is, self-driving capabilities), phones (for example, photo enhancement software), and applications you use every day (such as recommendation systems in your favorite video platform).

DL models are achieving and, at times, exceeding human accuracy on tasks such as computer vision (object detection and segmentation, image classification tasks, and image generation) and language tasks (translation, entity extraction, and text sentiment analysis). Beyond these areas, DL is also actively applied to complex domains such as healthcare, information security, robotics, and automation.

We should expect that DL applications in these domains will only grow over time. With current results and future promises also come challenges when implementing DL models. But before talking about the challenges, let’s quickly refresh ourselves on what DL is.

In this chapter, we will do the following:

• We’ll get a quick refresher on DL and its challenges
• We’ll provide an overview of Amazon SageMaker and its value proposition for DL projects
• We’ll provide an overview of the foundational SageMaker components – that is, managed training and hosting stacks
• We’ll provide an overview of other key AWS services

These will be covered in the following topics:

• Exploring DL with Amazon SageMaker
• Choosing Amazon SageMaker for DL workloads
• Exploring SageMaker’s managed training stack
• Using SageMaker’s managed hosting stack
• Integration with AWS services