Facilitated Session Schedule
| Event | Date | Spaces left |
|---|---|---|
| Enabling Enterprise-wide Data Science with OCS Dataviews - Virtual Live Classroom | 19 |
Enabling Enterprise-wide Data Science with OCS Dataviews
This course will show users how OCS extends PI infrastructure to support enterprise-wide advanced analytics and machine learning.
In this course, you will gain hands-on experience with a new OSIsoft software product, OSIsoft Cloud Services (OCS). OCS is a cloud-native platform built for historical, real-time and future/forecasted operational data. OCS complements existing on-premises PI systems and enables users to easily define, visualize, query and shape data sets required for data science.
By the end of the course, you will be able to...
- Use the features and functionalities in OCS
- Analyze & visualize data in OCS
- Add metadata & create Data Views in OCS
- Access OCS data in a Jupyter Notebook via Python
- Filter & prepare data from OCS for machine learning
- Use a machine learning model on a filtered OCS dataset
- Retrieve weather data from a Weather API into the trained machine learning model
- Send forecasted data back to OCS
Audience
This course is primarily suited for data scientists who are looking to extend operational data in their PI Systems for enterprise-wide machine learning applications.
Level: Intermediate
Study time: 16 hours
Course Access: Unlimited access. The only exception is the Training Cloud Environment for which you have 30 day access. After those 30 days you can purchase additional access with one of the two options below:
Prerequisites
- Basic understanding of PI Server components, including PI Data Archive and PI Asset Framework
- Basic understanding of Python, Jupyter Notebooks and machine learning
- A computer that can access our YouTube content, and pass our connection test
This Course Includes...
- Videos, discussion opportunities and quizzes to help you learn the material
- A sharable certificate of completion
Further Information
- This course assumes basic knowledge and understanding of wind turbines and their corresponding power curves. A brief introduction of wind turbine operation and relevant aspects of their power curves will be presented, but for additional information, please refer to the References section at the end.
- This is a self-paced course. Any questions or assistance needed about the material can be asked in this course's space in the OSIsoft PI Square community
- When you complete the examination at the end of the course, you will receive a certificate of completion which can be shared and directly posted on LinkedIn.
- For more information about our Online Courses please visit our FAQ page
You can audit the full video lecture content right now on the OSIsoft Learning YouTube Channel
- Getting Started
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PLEASE READ - Key Course Information
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Facilitated Session Schedule
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Course Grading Scheme
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How to Navigate This Course
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Offline Course Videos for Blocked YouTube Users
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Course Workbook
- Training Cloud Environment
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Cloud Environments Introduction
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Cloud Environments Instructions
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Launch Cloud Environment
- Lesson 1 - Introduction
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Introduction
- Lesson 2 - Background Knowledge
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Domain Knowledge – Machine Learning
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Review the Wind Farm AF Model in PI System Explorer
- Lesson 3 - Introducing and Exploring OCS
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Introducing OCS
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Logging into & Navigating OCS
- Lesson 4 - Managing metadata in OCS
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Metadata in OCS
- Lesson 5 - Creating Data Views in OCS
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OCS Data Views
- Lesson 6 - OCS clients
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OCS Clients
- Lesson 7 - Accessing Jupyter Notebook & Using API Console in OCS
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How to access Jupyter Notebook
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Introduction to OCS API Console and documentation
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Prepare and check URL calls to access data in OCS
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Getting data from data views
- Lesson 8 - Accessing OCS Data View and Preparing Dataset for Machine Learning
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Introduction
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Modify config.ini file and create new secret
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Wind Turbine script walkthrough – part 1
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Wind Turbine script walkthrough – part 2
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Create new data view for turbine states
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Wind Turbine script walkthrough – part 3 (Filters)
- Lesson 9 - Use Filtered Dataset to Train & Evaluate Machine Learning Model
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Introduction
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Prepare the training and testing data sets
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Use the Decision Tree Regression ML model
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Plot the results and save the ML model
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Test the model and run sample prediction
- Lesson 10 - Getting Forecast Weather Data into ML Model
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Introduction
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Retrieve weather forecast
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Store JSON response in data frame
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Use the ML model to predict Active Power
- Lesson 11 - Sending Data Back to OCS
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Introduction
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OMF overview
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OMF and connections
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Send data to OCS (create SDS Type)
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Send data to OCS (create Stream)
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Send data to Stream and trend
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Lab summary
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[PAGE] Useful Links
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Course Evaluation
- Final Exam
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Final Exam