All of Our ML Ideas Are Bad - and We Should Feel Bad

This course explores the limitations and challenges of using Machine Learning (ML) in production engineering. It addresses common proposed uses of ML in this field and explains why they are often not feasible. The course also discusses approaches to evaluating the feasibility of ML applications and emphasizes that while ML can solve some problems, it is not a universal solution. The intended audience for this course includes professionals in the field of production engineering, particularly those interested in applying ML technologies. The course covers topics such as the differences between Machine Learning and Artificial Intelligence, the shortcomings of using ML for ticket categorization and root cause analysis, and the impact of confirmation bias in ML applications.