Once a predictive model is built, tested and validated, you can easily deploy and use any machine learning model using MaaS_ML as a REST Web Service on a physical or a virtual compute host on which there is an available Proactive Node. This will be particularly useful for engineering or business teams that want to take advantage of this model. The life cycle of any MaaS_ML instance (i.e., from starting the generic service instance, deploying a machine learning model to pausing or deleting the instance) can be managed in three different ways in ProActive Machine Learning PML (PML is the new name for MLOS) that will be described in this tutorial.
In this tutorial, you will get introduced to managing MaaS_ML instances in PML via:
1 Management of a MaaS_ML Instance Using the Studio Portal
Using the Studio Portal of ProActive, we are able to manage the life cycle of a MaaS_ML instance i.e. starting a generic service instance, deploying a ML model to pausing or deleting the instance.
2 Management of a MaaS_ML Instance Using the Service Automation Portal
MaaS_ML instance lifecycle can be also managed using the Service Automation portal by following the steps below:
3 Management of a MaaS_ML Instance Using the Swagger UI
Once the MaaS_ML service is launched and running using in Service Automation Portal, click on the maas_ml-gui under Endpoint. In the Audit & Traceability page, click on the link provided in the top of the page to access the Swagger UI. Using the Swagger UI, a user is able to deploy a machine learning model as a service. When the ML model is deployed as a service, it can be called to apply some predictions for input datasets.