Train and host Scikit-Learn models in Amazon SageMaker by building a Scikit Docker container Amazon Web Services

Introduced at re:Invent 2017, Amazon SageMaker provides a serverless data science environment to build, train, and deploy machine learning models at scale. Customers also have the ability to work with frameworks they find most familiar, such as Scikit learn. In this blog post, we'll accomplish two goals: First, we'll give you a high-level overview of how Amazon SageMaker uses containers for training and hosting models. Second, we'll guide you through how to build a Docker container for training and hosting Scikit models in Amazon SageMaker. In the overview, we'll discuss how Amazon SageMaker runs Docker images that have been loaded from Amazon Elastic Container Service (ECS) for training and hosting models. We will also discuss the anatomy of a SageMaker Docker image, including the training code and inference code. If you are only interested in building, training, and deploying Scikit models in Amazon SageMaker, you can skip the overview. Instead, go right to the hands-on demonstration of how to containerize Scikit models in SageMaker with the minimal amount of effort. SageMaker makes extensive use of Docker containers to allow users to train and deploy algorithms. Containers allow developers and data scientists to package software into standardized units that run consistently on any platform that supports Docker. Containerization packages code, runtime, system tools, system libraries and settings all in the same place, isolating it from its surroundings, and insuring a consistent runtime regardless of where it is being run. When you develop a model in Amazon SageMaker, you can provide separate Docker images for the training code and the inference code, or you can combine them into a single Docker image.