When using CMLE, does creating a version require specifying a source of an exported model?
When using CMLE (Cloud Machine Learning Engine) to create a version, it is necessary to specify a source of an exported model. This requirement is important for several reasons, which will be explained in detail in this answer.
Firstly, let's understand what is meant by "exported model." In the context of CMLE, an exported model refers to a trained machine learning model that has been saved or exported in a format that can be used for prediction. This exported model can be stored in various formats such as TensorFlow SavedModel, TensorFlow Lite, or even a custom format.
Now, why is it necessary to specify a source of an exported model when creating a version in CMLE? The reason lies in the workflow of CMLE and the need to provide the necessary resources for serving the model. When creating a version, CMLE needs to know where the exported model is located so that it can be deployed and made available for prediction.
By specifying the source of the exported model, CMLE can efficiently retrieve the model and load it into the serving infrastructure. This allows the model to be ready for prediction requests from clients. Without specifying the source, CMLE would not know where to find the model and would not be able to serve predictions.
Additionally, specifying the source of the exported model enables CMLE to handle versioning effectively. In machine learning, it is common to train and iterate on models, improving them over time. CMLE allows you to create multiple versions of a model, each representing a different iteration or improvement. By specifying the source of the exported model, CMLE can keep track of these versions and ensure that the correct model is served for each prediction request.
To illustrate this, consider a scenario where a machine learning engineer trains a model using TensorFlow and exports it as a SavedModel. The engineer then uses CMLE to create a version of the model, specifying the source as the exported SavedModel file. CMLE deploys the model and makes it available for prediction. Now, if the engineer later trains an improved version of the model and exports it as a new SavedModel, they can create another version in CMLE, specifying the new exported model as the source. This allows CMLE to manage both versions separately and serve the appropriate model based on the version specified in prediction requests.
When using CMLE to create a version, specifying a source of an exported model is necessary to provide the necessary resources for serving the model, enable efficient retrieval and loading of the model, and support versioning of models.
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