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Course Outline
Introduction to Cursor for 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 in notebooks
Streamlining Notebook Development
- Managing and creating Jupyter notebooks within Cursor
- Leveraging AI for code completion, data exploration, and visualization
- Documenting experiments and ensuring reproducibility
Constructing ETL and Feature Engineering Pipelines
- Refactoring and generating ETL scripts with AI support
- Designing feature pipelines for scalable performance
- Applying version control to pipeline components and datasets
Model Training and Evaluation using Cursor
- Structuring model training code and evaluation loops
- Combining data preprocessing with hyperparameter tuning
- Safeguarding model reproducibility across different environments
Embedding Cursor into MLOps Pipelines
- Linking Cursor to model registries and CI/CD workflows
- Using AI-assisted scripts for automated retraining and deployment
- Tracking model lifecycle and versioning
AI-Supported Documentation and Reporting
- Producing inline documentation for data pipelines
- Generating experiment summaries and progress reports
- Enhancing team collaboration through context-linked documentation
Governance and Reproducibility in ML Projects
- Adopting best practices for data and model lineage
- Ensuring compliance and governance for AI-generated code
- Auditing AI decisions and maintaining traceability
Enhancing Productivity and Future Applications
- Employing prompt strategies to accelerate iteration
- Identifying automation potential in data operations
- Getting ready for future advancements in Cursor and ML integration
Wrap-up and Next Steps
Requirements
- Hands-on experience with Python for data analysis or machine learning
- Knowledge of ETL and model training processes
- Proficiency with version control systems and data pipeline tools
Target Audience
- Data scientists creating and refining ML notebooks
- Machine learning engineers architecting training and inference pipelines
- MLOps experts overseeing model deployment and reproducibility
14 Hours
