Deployability Aspects of AI
What it takes to put an AI system into production — latency, cost, robustness, monitoring and rollback.
§ 1Calendar
- Quiz 1—
- Quiz 2—
- End-term examination—
§ 2Previous Year Questions
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§ 3Resources
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Syllabus
The official course syllabus, transcribed from the IITM BS Data Science & Applications course booklet.
§ 1Course Description
The success of machine learning and deep learning models in laboratory settings often does not translate to real-world deployment. This course introduces the key challenges involved in deploying ML systems, including explainability, fairness, privacy, and security, with a focus on understanding these issues from an algorithmic and statistical perspective.
Lectures-Tutorials-Practicals-Credits: 4-0-0-4
§ 2Faculty
This course has not yet been introduced. Faculty and a week-by-week syllabus are not yet available — check the official handbook for the latest status.
§ 3Week-by-week
Week-by-week breakdown not yet available for this course.