Amazon’s Web Services have a series of optimized services specifically tailored for Artificial Intelligence and Machine Learning Algorithms. These fit into three major tiers, as follows:

Application Services — These are domain-based services which allow us to very quickly generate predictions with pre-trained models using simple API calls.

Platform Services — Unlike Application services, platform services allow us to build our customized Machine Learning and AI solutions through optimized and scalable options. The SageMaker service that we will discuss in this article, falls in this tier.

Frameworks and Hardware — The tiers mentioned above run on top of the frameworks and hardware tier. This layer provides a wide range of optimized deep learning tools like TensorFlow, Keras, Pytorch and Apache MXNet. Options of compute options (GPU, CPU) are also available.

Amazon SageMaker

Now that we know where Amazon’s SageMaker Service falls, lets delve a bit deeper into it.

A generic Machine Learning Pipeline has the following primary modules:

· Data Extraction

· Data Processing

· Data Analysis

· Feature Engineering

· Model Training and Tuning

· Prediction Generation

· Deployment to End-User

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