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Course Outline
Introduction to Cursor for Data and ML Workflows
- An overview of Cursor’s role in data and ML engineering
- Environment setup and connecting to data sources
- Understanding AI-powered code assistance within notebooks
Accelerating Notebook Development
- Creating and managing Jupyter notebooks inside Cursor
- Leveraging AI for code completion, data exploration, and visualization
- Documenting experiments and ensuring reproducibility
Building ETL and Feature Engineering Pipelines
- Generating and refactoring ETL scripts with AI assistance
- Structuring feature pipelines for scalability
- Applying version control to pipeline components and datasets
Model Training and Evaluation with Cursor
- Scaffolding model training code and evaluation loops
- Integrating data preprocessing and hyperparameter tuning
- Ensuring 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 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 when using AI-generated code
- Auditing AI decisions to maintain traceability
Optimizing Productivity and Future Applications
- Applying effective prompt strategies for faster iteration
- Exploring automation opportunities in 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
- 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 refining ML notebooks
- Machine learning engineers designing training and inference pipelines
- MLOps professionals responsible for managing model deployment and reproducibility
14 Hours
