EP236U Machine Learning in Production 5.0 credits

• Lecture 1: Introduction
• Lecture 2: Data ingestion and analysis
• Lab 1: Data ingestion and analysis
• Lecture 3: High-performance machine learning development
• Lab 2: Model development 
• Lecture 4: Model deployment and testing
• Lab 3: Model deployment and testing 
• Lecture 5: Observability
• Lab 4: Observability
• Lecture 6: Privacy and security
• Lecture 7: Machine learning at the edge

After passing the course, the student should be able to:

• summarise a real machine learning ecosystem in production where the model is one of many different components
• explain the entire end-to-end data pipeline i.e. from data collection and storage to model deployment and monitoring
• deploy a machine learning model
• interpret data and handle properties of real data
• develop batch and online interfaces
• discuss model versioning and testing
• evaluate privacy and security in machine learning
• discuss edge learning and applications of machine learning for Internet of Things

• INL1 - Home assignments, 4.0 credits, grading scale: P, F
• DEL1 - Workshop, 1.0 credits, grading scale: P, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

If the course is discontinued, students may request to be examined during the following two academic years.

Carlo Fischione