- Review the standard ML use-case cycle from “I have an idea” to “I can monitor my ML
application running in production and quantify the benefits of having such a product”
- Translation of a business problem to a Machine Learning project. Review of the
importance of defining the metrics of interest
- Review the modern way of running a ML project. Exploration of cloud services and in
particular AutoML features allowing for fast prototyping
- Illustration of concepts using use-cases covered in previous modules, embedding in
particular topics such as ML bias, cloud costs as well as regulatory and ethical topics.
Overarching view of the ML product with emphasis on ROI.
- Business cases and practical examples with real data using the Machine Learning platform
“BigML.com” and original data) – no programming required!
- Dr. Christopher Bruffaerts, University of Fribourg
Subject Area Co-ordinator
- Prof. Dr. Michael Burkert, University of Fribourg
Le programme est vide