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Course Outline
Introduction to Cursor for Data and ML Workflows
- Overview of Cursor’s function in data and ML engineering
- Configuring the environment and linking data sources
- Grasping AI-driven code assistance within notebooks
Expediting Notebook Development
- Creating and managing Jupyter notebooks inside Cursor
- Utilising AI for code completion, data exploration, and visualisation
- Recording experiments and upholding reproducibility
Constructing ETL and Feature Engineering Pipelines
- Generating and refactoring ETL scripts with AI
- Structuring feature pipelines for scalability
- Managing version control for pipeline components and datasets
Model Training and Evaluation with Cursor
- Scaffolding model training code and evaluation loops
- Integrating data preprocessing and hyperparameter tuning
- Safeguarding model reproducibility across different environments
Embedding Cursor into MLOps Pipelines
- Connecting Cursor to model registries and CI/CD workflows
- Employing AI-assisted scripts for automated retraining and deployment
- Monitoring the model lifecycle and tracking versions
AI-Assisted Documentation and Reporting
- Generating inline documentation for data pipelines
- Creating experiment summaries and progress reports
- Enhancing team collaboration through context-linked documentation
Reproducibility and Governance in ML Projects
- Implementing best practices for data and model lineage
- Maintaining governance and compliance with AI-generated code
- Auditing AI decisions and preserving traceability
Optimising Productivity and Future Applications
- Applying prompt strategies for faster iteration
- Exploring automation opportunities in data operations
- Preparing for future Cursor and ML integration advancements
Summary and Next Steps
Requirements
- Practical experience with Python-based data analysis or machine learning
- Knowledge of ETL and model training workflows
- Proficiency with version control and data pipeline tools
Audience
- Data scientists developing and refining ML notebooks
- Machine learning engineers designing training and inference pipelines
- MLOps professionals overseeing model deployment and reproducibility
14 Hours
