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,
How can you call predictions using a sample row of data on a deployed scikit-learn model on Cloud ML Engine?
To call predictions using a sample row of data on a deployed scikit-learn model on Cloud ML Engine, you need to follow a series of steps. First, ensure that you have a trained scikit-learn model that is ready to be deployed. Scikit-learn is a popular machine learning library in Python that provides various algorithms for
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Advancing in Machine Learning, Scikit-learn models at scale, Examination review
What are the steps involved in using Google Cloud Machine Learning Engine's prediction service?
The process of using Google Cloud Machine Learning Engine's prediction service involves several steps that enable users to deploy and utilize machine learning models for making predictions at scale. This service, which is part of the Google Cloud AI platform, offers a serverless solution for running predictions on trained models, allowing users to focus on