Google Cloud ML Engine: Train, and Deploy Machine Learning Models

The Google Cloud ML Engine is a hosted platform to run machine learning training jobs and predictions at scale. The service treats these two processes (training and predictions) independently. It is possible to use Google Cloud ML Engine just to train a complex model by leveraging the GPU and TPU infrastructure. The outcome from this step — a fully-trained machine learning model — can be hosted in other environments including on-prem infrastructure and public cloud. The service can also be used to deploy a model that is trained in external environments. Cloud ML Engine automates all resource provisioning and monitoring for running the jobs. It can also manage the lifecycle of deployed models and their versions.

Apart from training and hosting, Cloud ML Engine can also perform hyperparameter tuning that influences the accuracy of predictions. Without automated hyperparameter tuning, data scientists will have to experiment with multiple values while evaluating the accuracy of the results.

 Let’s take a closer look at the steps involved in training and predicting from a machine learning model deployed in Google Cloud ML Engine.

Read the entire article at The New Stack

Janakiram MSV is an analyst, advisor, and architect. Follow him on Twitter,  Facebook and LinkedIn.