How can we create a static model for serving predictions in TensorFlow?
Wednesday, 02 August 2023
by EITCA Academy
To create a static model for serving predictions in TensorFlow, there are several steps you can follow. TensorFlow is an open-source machine learning framework developed by Google that allows you to build and deploy machine learning models efficiently. By creating a static model, you can serve predictions at scale without the need for real-time training
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, First steps in Machine Learning, Serverless predictions at scale, Examination review
Tagged under:
Artificial Intelligence, Google Cloud, Machine Learning, Serving Predictions, Static Model, TensorFlow
What is the purpose of Google's Cloud Machine Learning Engine in serving predictions at scale?
Wednesday, 02 August 2023
by EITCA Academy
The purpose of Google's Cloud Machine Learning Engine in serving predictions at scale is to provide a powerful and scalable infrastructure for deploying and serving machine learning models. This platform allows users to easily train and deploy their models, and then make predictions on large amounts of data in real-time. One of the main advantages
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