Is it recommended to serve predictions with exported models on either TensorFlowServing or Cloud Machine Learning Engine's prediction service with automatic scaling?
When it comes to serving predictions with exported models, both TensorFlowServing and Cloud Machine Learning Engine's prediction service offer valuable options. However, the choice between the two depends on various factors, including the specific requirements of the application, scalability needs, and resource constraints. Let us then explore the recommendations for serving predictions using these services, taking into account their features, advantages, and limitations.
TensorFlowServing is an open-source serving system specifically designed for TensorFlow models. It provides a flexible and efficient way to serve predictions at scale. TensorFlowServing allows you to deploy your models on a server and expose them through a well-defined API. This enables easy integration with other systems and applications. One of the key advantages of TensorFlowServing is its ability to handle large-scale deployments and high-throughput scenarios. It supports automatic scaling, allowing you to handle increased prediction requests by adding more instances of the serving system. This makes it suitable for applications with fluctuating prediction demands or those requiring rapid scalability.
On the other hand, Cloud Machine Learning Engine's prediction service is a managed service provided by Google Cloud. It offers a convenient and fully-managed environment for serving predictions using machine learning models, including TensorFlow models. The prediction service abstracts away the infrastructure management, making it easy to deploy and serve models without worrying about scalability or resource provisioning. It also provides automatic scaling based on the incoming prediction traffic, ensuring that the service can handle varying workloads effectively. This makes Cloud Machine Learning Engine's prediction service an excellent choice for applications that require a hassle-free and scalable prediction serving solution.
To determine which option is recommended, it is important to consider the specific requirements and constraints of the application. If you prefer a self-managed serving solution with fine-grained control over the infrastructure and deployment process, TensorFlowServing is a solid choice. This option is particularly useful when you have specific hardware requirements, such as utilizing Tensor Processing Units (TPUs) for accelerated inference. TensorFlowServing allows you to leverage TPUs by integrating them into your serving infrastructure.
On the other hand, if you prioritize ease of use, scalability, and a fully-managed environment, Cloud Machine Learning Engine's prediction service is the way to go. This option is ideal when you want to focus on the model and its predictions rather than managing the underlying infrastructure. The prediction service automatically scales based on the incoming traffic, ensuring that your predictions can handle varying workloads without manual intervention.
Both TensorFlowServing and Cloud Machine Learning Engine's prediction service offer valuable options for serving predictions with exported models. TensorFlowServing provides a flexible and scalable serving solution, particularly suitable for applications with specific hardware requirements. Cloud Machine Learning Engine's prediction service, on the other hand, offers a fully-managed environment with automatic scaling, making it an excellent choice for applications that prioritize ease of use and scalability.
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