Hands-On Industrial Internet of Things

ML Engine

The process to develop a model using the ML Engine is quite similar to that we have seen in the Working with the Azure ML service and Implementing analytics on AWS SageMaker sections:

1. Develop the algorithm locally
2. Train the model using local data
3. Publish the model on GCP Cloud
4. Use ML Engine to perform a more advanced training step, using GPU, CPU or TPU
5. Deploy the model on the cloud

The following diagram shows the workflow proposed by GCP:

Engine ML workflow (source: Google Cloud Platform)

The basic concept of Engine ML is to use the gcloud ml-engine command-line tool and the Google Cloud libraries to train the model locally and ...