Machine Learning Operations

Introduce the student to several tools and software development practices that will help them organize, scale, deploy and monitor machine learning models either in a research or production setting. To provide hands-on experience with a number of frameworks, both local and in the cloud, for working with large scale machine learning pipelines.

At the end of the course, a learner will be able to: Organize code in an efficient way for easy maintainability and shareability ; Capable of using version control systems to efficiently collaborate on code development and handle large amounts of data ; Being able to create reproduceable software environments and reproduceable containerized applications and experiments ; Being able to debug, profile, visualize and monitor multiple experiments to assess model performance ; Implement basic testing of software and apply continuous integration (CI) for automating code development ; Capable of using cloud based computing services to scale experiments and automate processes ; Able to deploy machine learning models, both locally and in the cloud and monitor the lifecycle of the model after deployment ; Demonstrate how to scale data loading, training and inference of the machine learning pipeline using distributed frameworks and optimization strategies ; Conduct a research project in collaboration with follow students using the frameworks taught in the course.

Evaluation of assignment(s)/report(s)

General understanding of machine learning (datasets, probability, classifiers, overfitting etc.) and basic knowledge about deep learning (backpropagation, convolutional neural networks, auto-encoders etc.). Familiar with coding in Pytorch.

• https://skaftenicki.github.io/dtu_mlops/

The course includes lectures, exercises and project work. Approximately 30% of the course is spent on project work in groups of 3-5 persons, where tools throughout the course should be applied on a self-chosen machine learning problem.