This article provides a step by step explanation of how to get started with AWS Sagemaker by creating an AWS SageMaker instance for using machine learning related features on AWS Cloud.

Introduction

Before the advent of cloud, machine learning (ML) and artificial intelligence (AI) was limited to organizations and professionals who had the financial power to afford the required hardware and software, as well as expertise to operate machine learning algorithms and applications. Cloud has democratized access to machine learning and artificial intelligence by packaging AI/ML hardware and software in an affordable pricing model, as well as abstracting the huge complexity of setting up the environment itself for kick-starting AI/ML applications. AWS SageMaker is the primary and key service on the AWS cloud that provides a platform to build ML applications at scale. It comes packed with several machine learning frameworks like Tensorflow, MXNet, etc., AWS SDKs, and programming languages like Python.

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