Google AI Platform vs Google App Engine: What are the differences?

Introduction

Google AI Platform and Google App Engine are two services provided by Google that are used for different purposes. While Google AI Platform is designed for training and deploying machine learning models, Google App Engine is a platform for building and hosting web applications.

1. Flexibility and Purpose: Google AI Platform is primarily focused on the development and deployment of machine learning models. It provides a range of tools and resources specific to machine learning tasks, such as data processing, model training, and hyperparameter tuning. On the other hand, Google App Engine is more flexible and can be used for a wider range of web application development tasks, including deployment, scaling, and managing web applications.

2. Infrastructure Management: With Google AI Platform, the infrastructure management is abstracted away, allowing developers to focus on model training and deployment. Google manages the underlying infrastructure, including storage, compute resources, and networking. In contrast, Google App Engine offers more control over the infrastructure management, allowing developers to fine-tune and customize the deployment environment to their specific needs.

3. Scalability: Both Google AI Platform and Google App Engine are designed to handle scalable workloads. However, Google App Engine provides automatic scaling based on the application's traffic and resource usage. It can automatically scale up or down to handle fluctuations in the workload. Google AI Platform, on the other hand, provides scalable training and inference capabilities for machine learning tasks, but the scalability of the overall system needs to be managed by the developer.

4. ML-specific Features: Google AI Platform provides numerous features specifically designed for machine learning tasks, such as distributed training, hyperparameter tuning, and serving predictions at scale. It offers integration with Google Cloud's extensive set of machine learning tools, including TensorFlow, PyTorch, and scikit-learn. Google App Engine, on the other hand, does not have these ML-specific features and is more focused on web application development.

5. Pricing Model: The pricing model differs between Google AI Platform and Google App Engine. Google AI Platform primarily charges based on the usage of compute resources, storage, and network egress used during model training and serving. Google App Engine, on the other hand, follows a more traditional web hosting pricing model, charging based on the instance class, instance hours, and network egress for the web application.

6. Development Workflow: When using Google AI Platform, developers typically follow a specific workflow for developing and deploying machine learning models. This involves preparing and preprocessing the data, selecting and training the model, tuning hyperparameters, and finally serving the model for predictions. Google App Engine, on the other hand, follows a more standard web application development workflow, focusing on building and deploying web applications using frameworks like Flask or Django.

In summary, Google AI Platform is specifically designed for machine learning tasks, providing ML-specific features and abstracting away infrastructure management. Google App Engine, on the other hand, is a more general-purpose platform for building and hosting web applications with more flexibility and control over infrastructure management.