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AWS SageMaker vs. Google AI Platform

October 25, 2021

Introduction

As businesses turn to the cloud for machine learning solutions, the choice of platform makes all the difference. In this post, we'll do a side-by-side comparison of two popular options: AWS SageMaker and Google AI Platform. We'll take a look at the key features and benefits of both platforms to help you make a more informed decision.

1. Cost

Cost is an important consideration when choosing a machine learning platform. AWS SageMaker is priced based on usage, with fees for training, inference, and storage. Google AI Platform has a similar pricing structure, but their ML solutions are often more cost-efficient.

2. Data processing

Both platforms offer comprehensive data preparation and processing capabilities. AWS SageMaker has one advantage with its ability to scale horizontally across a large number of instances. Meanwhile, Google AI Platform has an early processing advantage when it comes to pre-processing large quantities of data.

3. Model training

AWS SageMaker provides flexibility and control over the model training process. The platform also has a wide range of pre-built machine learning algorithms and frameworks, making it a good choice for those who want to customize their models. Google AI Platform, on the other hand, has automatic hyperparameter tuning, which can help save time during the training process.

4. Model deployment

Both platforms allow for easy deployment and monitoring of models, but AWS SageMaker allows for more customization and control over deployments. Meanwhile, Google AI Platform offers a simple, user-friendly interface for deploying models.

5. APIs and integrations

Both platforms offer APIs for easy integration with other tools and services. AWS SageMaker has a more extensive suite of APIs, while Google AI Platform has stronger links with Google Cloud Services.

References

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