Machine Learning Operations

• 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

Proper coding environments, code organization, good coding practices, code and data version control, reproducible and containerized environments, reproducible experiment management, debugging tools, code profiling, large scale collaborative experiment logging and monitoring, unit testing, continuous integration, continuous machine learning, cloud infrastructure, cloud based machine learning, distributed data loading and training, optimization methods for inference, local and cloud based deployment, monitoring of deployed applications.

02456, 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.

The course includes lectures, exercises and project work. The lectures are short and provide context for why each topic is important. The main focus is on exercises with emphasis on practical tools and coding skills for machine learning in production. Finally, approximately 30% of the course is spent on project work in groups of 3-5 persons, where tools used throughout the course should be applied on a self-chosen machine learning problem.