Course Description

In this course, you will gain insight into the operationalization of machine learning (ML) solutions and engage in practical, hands-on learning to discover the best practices for constructing ethical AI solutions. Through this exploration, you will learn to recognize and understand the framework for managing models, documentation, explainability and reproducibility.

To acquire the ability to apply developed models to real-world scenarios in a production environment, you will deploy models as microservices and integrate them into end-user applications. Finally, with a focus on how end-users consume data, you will learn to present scored data through an interactive dashboard.

For those interested in coding, there is an optional component on using Python to build, assess and deploy ML models.

Learner Outcomes

Upon completing this course, you will know how to:

  • publish ML models
  • deploy ML models as microservices
  • integrate deployed microservices/API models into an end-user application
  • build an interactive dashboard on scored data.


DATA 025 - Deep Learning and Unsupervised Learning course is a mandatory requirement for this course.

Applies Towards the Following Certificates

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Section Title
Artificial Intelligence Governance and Machine Learning Operations
Lecture - Online Synchronous
6:00PM to 7:00PM
Sep 17, 2024 to Oct 15, 2024
Schedule and Location
Contact Hours
Delivery Options
Course Fee(s)
Tuition Fee non-credit $1,935.00
Potential Discount(s)
Section Notes


DATA 025 - Deep Learning and Unsupervised Learning


Participants will complete 19 hours of online, independent study supplemented with 5 hours of live virtual learning sessions with the instructor and other students. These virtual sessions are mandatory and will be delivered using Zoom & Brightspace/D2L and are interactive. Zoom sessions will be held on Tuesdays starting September 17, 2024 and ending October 15, 2024, from 6:00 pm - 7:00 pm. 

We encourage you to use your webcam and microphone to contribute to a more collaborative learning experience.

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