Build a Model in SageMaker over 5 steps Using High-Level API

AWS ML Model Development Best Practices

AWS (or Amazon) SageMaker is a fully managed service that provides the ability to build, train, tune, deploy, and manage large-scale machine learning (ML) models quickly.

Sagemaker provides tools to make each of the following steps as shown in the below image, simpler.

Source: https://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works-training.html

In this article, we will be looking at each of the above steps. For better understanding, let us divide this project into two articles:

1. Build a model in SageMaker in 5 steps using high-level API
2. Deploy a Model in SageMaker in 5 steps

For further understanding of how low-level API works, please refer to the articles mentioned below. If you are a beginner, start with the articles for high-level API before you move onto the article on low-level API.

3. Build a model in SageMaker in 5 steps using low-Level API

Before we get started, for beginners with no AWS account, create 12 months of free tier access AWS account.

This project’s focus is not on the model itself, instead of on the steps involved in modeling on SageMaker. Let us get onto building a model in AWS SageMaker in 5 simple steps. If you would like to…