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Duration 14 hours
Course Outline
Introduction to Cursor in Data and ML Workflows
- The role of Cursor in data and ML engineering
- Environment setup and data source integration
- How AI-powered code assistance functions within notebooks
Accelerating Notebook Development
- Creating and managing Jupyter notebooks using Cursor
- Leveraging AI for code completion, data exploration, and visualization
- Documenting experiments to ensure reproducibility
Developing ETL and Feature Engineering Pipelines
- Generating and refactoring ETL scripts with AI support
- Designing feature pipelines for scalability
- Managing version control for pipeline components and datasets
Model Training and Evaluation via Cursor
- Building scaffolds for model training code and evaluation loops
- Integrating data preprocessing and hyperparameter tuning
- Guaranteeing model reproducibility across different environments
Integrating Cursor into MLOps Pipelines
- Connecting Cursor to model registries and CI/CD workflows
- Utilizing AI-assisted scripts for automated retraining and deployment
- Monitoring model lifecycle and tracking versions
AI-Assisted Documentation and Reporting
- Generating inline documentation for data pipelines
- Producing 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 when using AI-generated code
- Auditing AI decisions to ensure traceability
Productivity Optimization and Future Applications
- Applying prompt strategies to accelerate iteration
- Identifying automation opportunities within data operations
- Preparing for future advancements in Cursor and ML integration
Summary and Next Steps
Requirements
- Practical experience with Python-based data analysis or machine learning
- Conceptual understanding of ETL and model training workflows
- Familiarity with version control systems and data pipeline tools
Target Audience
- Data scientists developing and refining ML notebooks
- Machine learning engineers architecting training and inference pipelines
- MLOps specialists overseeing model deployment and reproducibility
