Amazon Elastic Inference vs Amazon SageMaker: What are the differences?

Introduction

Amazon Elastic Inference and Amazon SageMaker are two services provided by Amazon Web Services (AWS) that are tailored for machine learning tasks. Although they are both related to machine learning, there are several key differences between the two that set them apart. The following paragraphs outline these differences in detail.

1. Amazon Elastic Inference: Amazon Elastic Inference is a service that allows customers to attach low-cost GPU-powered inference acceleration to Amazon EC2 instances. It provides GPU acceleration for deep learning inference workloads, making it possible to reduce the cost of running inference by sharing a single GPU among multiple instances. With Elastic Inference, you only pay for the GPU acceleration when it is used, resulting in cost savings.

2. Amazon SageMaker: Amazon SageMaker, on the other hand, is a fully managed end-to-end machine learning service that enables data scientists and developers to easily build, train, and deploy machine learning models at scale. It provides a wide range of capabilities, including data labeling, model training, hyperparameter tuning, and model hosting. SageMaker makes it seamless to build and deploy machine learning models using pre-built algorithms or custom-built models.

3. Different Focus: While both services are related to machine learning, Elastic Inference is primarily focused on inference acceleration, allowing the cost-effective deployment of models at scale. On the other hand, SageMaker provides a comprehensive platform for the entire machine learning lifecycle, from data preparation to model deployment, catering to the broader needs of data scientists and developers.

4. Deployment Complexity: Elastic Inference is designed to be easily integrated into existing Amazon EC2 instances, requiring minimal changes to the machine learning models. It aims to simplify the deployment process and reduce the cost of running inference workloads. In contrast, SageMaker offers a more comprehensive and customizable deployment process, allowing for greater flexibility and control over the deployment environment.

5. Training Capabilities: While Amazon Elastic Inference focuses on inference acceleration, Amazon SageMaker provides a rich set of tools and features for model training. SageMaker supports a variety of training algorithms, distributed training, and automatic model tuning capabilities. It also provides a large selection of pre-built algorithms that can be used out of the box, making it easier to train models without extensive coding or infrastructure setup.

6. Management and Monitoring: Another key difference between Elastic Inference and SageMaker is the level of management and monitoring provided. Elastic Inference is a more lightweight service that requires less management overhead as it is primarily focused on inference acceleration. In contrast, SageMaker offers extensive management and monitoring capabilities, allowing users to track the performance of their models, monitor resource utilization, and manage the entire machine learning lifecycle.

In summary, Amazon Elastic Inference is a service that provides GPU acceleration for inference workloads at a low cost, while Amazon SageMaker is a comprehensive machine learning service that covers the entire machine learning lifecycle. Elastic Inference is focused on making inference deployment cost-effective, while SageMaker offers a wider range of capabilities including data labeling, model training, hyperparameter tuning, and model hosting.