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Duration 14 hours (2 days)
Course Outline
Introduction to Cursor for Data and ML Workflows
- Overview of Cursor's strategic role in data and ML engineering
- Setting up the development environment and establishing data source connections
- Comprehending AI-powered code assistance features within notebooks
Accelerating Notebook Development
- Creating and managing Jupyter notebooks efficiently within Cursor
- Leveraging AI for automated code completion, data exploration, and visualisation
- Documenting experiments to maintain high levels of reproducibility
Building ETL and Feature Engineering Pipelines
- Generating and refactoring ETL scripts with AI support
- Structuring feature pipelines to ensure scalability
- Implementing version control for pipeline components and datasets
Model Training and Evaluation with Cursor
- Scaffolding model training code and establishing evaluation loops
- Integrating data preprocessing and hyperparameter tuning processes
- Ensuring consistent model reproducibility across different environments
Integrating Cursor into MLOps Pipelines
- Connecting Cursor to model registries and CI/CD workflows
- Using AI-assisted scripts for automated retraining and deployment
- Monitoring the model lifecycle and managing version tracking
AI-Assisted Documentation and Reporting
- Generating inline documentation for complex data pipelines
- Creating comprehensive 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 standards with AI-generated code
- Auditing AI decision-making processes and ensuring traceability
Optimizing Productivity and Future Applications
- Applying effective prompt strategies for faster iteration cycles
- Exploring automation opportunities within data operations
- Preparing for future advancements in Cursor and ML integrations
Summary and Next Steps
Requirements
- Practical experience in Python-based data analysis or machine learning
- A solid understanding of ETL and model training workflows
- Familiarity with version control systems and data pipeline tools
Target Audience
- Data scientists focused on building and iterating ML notebooks
- Machine learning engineers designing robust training and inference pipelines
- MLOps professionals responsible for managing model deployment and ensuring reproducibility
