Administrative information

Learning Events to be Completed Before

• None

Obligatory for Students

• ML-Ops principles
• Overview of testing
• CICD testing

Optional for Students

• Data Preparation and Management: Before diving into MLOps, it's beneficial to understand the initial phases of the machine learning process, especially data collection, cleaning, and preprocessing
• Model Training and Validation: A grasp of how models are trained, validated, and evaluated will provide a solid foundation for understanding the operational aspects of ML.
• Hyperparameter Tuning: While not always covered in depth in MLOps courses, understanding hyperparameter tuning can be beneficial as it's a crucial step in model optimization.
• MLOps Tools and Platforms: Familiarity with tools like Kubeflow, Azure ML, and others can give students a head start.
• Documentation Practices in ML: Proper documentation is essential in MLOps for reproducibility and collaboration. Understanding best practices in ML documentation can be advantageous.
• CRISP-DM, CRISP-ML, ML Canvas: These are methodologies and frameworks for ML project management. Having a basic understanding can be beneficial for the operational side of ML projects.

References and background for students:

• Coursera: Machine Learning Engineering for Production (MLOps) Specialization
• Coursera: Machine Learning Operations (MLOps): Getting Started
• MLOps course on github

Recommended for Teachers

• N/A