Travis Cox, Kathy Applebaum, and Kevin McClusky from Inductive Automation will discuss key concepts and best practices, show demos, and answer questions from the audience, to help you start integrating ML into your day-to-day processes. Learn more about: • Practical ways to use ML in your factory or facility • What you'll need to get started • Existing ML tools and platforms • And more

2. Moderator/Presenter
Travis Cox
Co-Director of Sales Engineering
Inductive Automation

6. Co-Presenters
Kevin McClusky
Co-Director of Sales
Engineering,
Inductive Automation
Kathy Applebaum
Senior Software
Developer,
Inductive Automation

7. • Machine learning has existed for a while but has
recently come to the forefront. Many companies
want to use machine learning but aren’t sure what
is it or what to use it for.
• ML has tremendous potential but it won’t give you
instant results. You must answer the right
questions and plan your project carefully.
• Make sure you have the right data, people, and
processes. If done right, a ML project can save
huge amounts of money and greatly increase your
speed after only a few months of development.
“What should we do about machine learning?”

8. What We’ll Cover Today
We’d like to help by discussing:
• What machine learning is
• Ways to apply it
• Steps to getting started with machine learning
Plus:
• Demo of Azure ML Studio
• Answering some of your questions

9. What is Machine Learning?
Three main branches:
• Analytics
• Machine Learning
• Artificial Intelligence

10. What is Machine Learning?
Three main branches:
• Analytics – Knowledge discovery
• Descriptive
• Diagnostic
• Predictive
• Prescriptive

11. What is Machine Learning?
Three main branches:
• Analytics – Knowledge discovery
• Machine Learning – Learn and improve from experience

12. What is Machine Learning?
Three main branches:
• Analytics – Knowledge discovery
• Machine Learning – Learn and improve from experience
• Artificial Intelligence – Tasks that simulate human intelligence

13. Software Options for Machine Learning

14. Machine Learning Demo in Ignition

15. What is Machine Learning?
Two main types of ML models:
• Classifiers – predict a category
• Regression – predict a value

16. Machine Learning Applications
#1 application: Predictive Analytics / Predictive Maintenance
Examples:
• Predicting when a motor will fail
• Predicting when a delivery truck will break down

17. Machine Learning Applications
In addition to predictive analytics/maintenance, there are many other
industrial ML applications, including:
• Predicting machine settings
• Quality control
• Demand forecasting
• Raw-material price forecasting
• Training industrial robots

18. Steps to Machine Learning
You need data – lots and lots of quality data!
• Collect data from a variety of sources: historical, ERP, etc.
• Automated data collection, not manual
• Quality of data more important than quantity

19. Steps to Machine Learning
You also need a dedicated person with statistics knowledge and domain
knowledge who can label data as good or bad.

20. Steps to Machine Learning
Pick a question to answer.
• Start with what you really want to know
• Cost function

21. Steps to Machine Learning
Use domain knowledge.
• What data might answer your question?
• Can you acquire missing data?
• What quality is your data?
• Eliminate dependent variables

22. Steps to Machine Learning
ETL (Extract, Transform, Load)
• Can you automate each step?
• Can new data be acquired automatically?
• How much clean-up is needed?
• How will missing values be handled?

23. Steps to Machine Learning
Visualize your data.
Things to look for:
• Problem data
• Obvious trends
• An obvious algorithm

24. Steps to Machine Learning
Determine which algorithm to use.
Narrow it down by asking:
• Classification vs. regression?
• Labeled vs. unlabeled?
• Tolerant of missing data?
• Lazy learning? Retraining?
• Black box vs. human readable
• Computing resources available
• Tolerant of outliers?

25. Steps to Machine Learning
Determine which platform to use.
Things to think about:
• Computing resources on-site or in the cloud?
• How flexible is it?
• How easy to get data into it?
• How easy to get results into a usable form? Can it be automated?

26. Steps to Machine Learning
Test your model.
• Testing techniques
• How accurate?
• Back up a few steps when needed

27. Software Options for Machine Learning
More Control More Automated

28. Azure ML Studio Demo

29. Ignition & Machine Learning
Ignition can be a great asset in an effective ML
solution:
• All the data you need to start
• Connects to ML platforms
• Ignition & Cirrus Link MQTT Transmission Module
connect to AWS Greengrass for machine learning
• Cirrus Link Cloud Modules enable easy
connection of Ignition tag data into AWS or Azure
• Easier access to machine learning & analytics
algorithms coming in Ignition v7.9.8

30. • Machine learning is related to analytics and AI
• Two main ML models are Classifiers (predict
category) and Regression (predict value)
• There are many industrial ML applications; the #1
application is predictive analytics/maintenance
• To get started, you need a huge amount of data
and a dedicated person who is qualified to sort it
• Other steps: picking a question to answer, using
domain knowledge, extract-transform-load,
visualize the data, choose an algorithm, choose a
platform, and test the model
• Ignition can be part of an effective ML solution
Recap

31. Design Like A Pro Series
inductiveautomation.com/
resources

34. Sept. 17-19, 2018
Early-bird tickets on sale now at:
icc.inductiveautomation.com

35. Questions & Comments
Jim Meisler x227
Vannessa Garcia x231
Vivian Mudge x253
Account Executives:
Myron Hoertling x224
Shane Miller x218
Ramin Rofagha x251
Maria Chinappi x264
Lester Ares x214
Kristen Azure x260
Director of Sales:
Melanie Hottman
800-266-7798 x247
Co-Directors of Sales Engineering:
Travis Cox - x229
travis@inductiveautomation.com
Kevin McClusky - x237
kmcclusky@inductiveautomation.com