Welcome to the IBM Maximo Monitor Auto AI Lab
(Version: 8.5)

You will learn about Monitor's capabilities to use AutoAI to identify and deploy a prediction machine learning model to Maximo Application Suite

In this lab we will show you how to use AutoAI to identify, train and then deploy to a prediction machine learning model to Maximo Asset Monitor. You will learn how to:

1. Use the provided Jupyter notebook that will contain Python code to train, test and deploy machine learning model to Maximo Asset Monitor

2. Use the provided Maximo Asset Monitor custom function to make predictions as new time series data is received in Maximo Asset Monitor

3. Create Asset Types and devices using simulated pump data.

4. Create an Asset Type and Asset dashboards to see the pump data and model predictions in Monitor

Prerequisites

This Hands on Lab requires:

• Your instructor can provide you the data, example Python Scripts, functions and Notebooks.

• An account for IBM ID and An IBM Cloud Account Trial here

• Access to a Maximo Asset Monitor environment. Request access from your instructor.

• Understand Virtual Environments

• Setup a local development environment. Check if you instructor has already provided you access to one.

• Introduction to using Jupyter Notebooks

• Understanding of Pandas for Time series data processing:

  • https://medium.com/analytics-vidhya/module-6-pandas-5b053ee1e1a1

  • Basic Time Series Manipulation with Pandas

  • Time series / date functionality

• Internet access to these tutorial directions

Steps

Follow the Getting Started instructions on the next page.

Learn more

• This tutorial is part of the Maximo Hands On Labs for Data scientists and Developers.

• Maximo Application Suite: Enjoyed this Tutorial? Check out our other Maximo Code Patterns.

• Knowledge Center Maximo Application Suite Documentation

License

This Hands on Lab is licensed under the Apache Software License, Version 2. Separate third party code objects invoked within this lab are licensed by their respective providers pursuant to their own separate licenses. Contributions are subject to the Apache Software License, Version 2.