How does Google Cloud’s serverless prediction capability simplify the deployment and scaling of machine learning models compared to traditional on-premise solutions?
Google Cloud's serverless prediction capability offers a transformative approach to deploying and scaling machine learning models, particularly when compared to traditional on-premise solutions. This capability is part of Google Cloud's broader suite of machine learning services, which includes tools like AI Platform Prediction. The serverless nature of these services provides significant advantages in terms of
What is the meaning of the term serverless prediction at scale?
The term "serverless prediction at scale" within the context of TensorBoard and Google Cloud Machine Learning refers to the deployment of machine learning models in a way that abstracts away the need for the user to manage the underlying infrastructure. This approach leverages cloud services that automatically scale to handle varying levels of demand, thereby
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, First steps in Machine Learning, Serverless predictions at scale
What are the primary options for serving an exported model in production?
When it comes to serving an exported model in production in the field of Artificial Intelligence, specifically in the context of Google Cloud Machine Learning and Serverless predictions at scale, there are several primary options available. These options provide different approaches to deploying and serving machine learning models, each with their own advantages and considerations.