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Course Outline
Introduction to Cursor for Data and ML Workflows
- Overview of Cursor’s role in data and ML engineering
- Setting up the environment and connecting data sources
- Understanding AI-powered code assistance in notebooks
Accelerating Notebook Development
- Creating and managing Jupyter notebooks within Cursor
- Using AI for code completion, data exploration, and visualization
- Documenting experiments and maintaining reproducibility
Building ETL and Feature Engineering Pipelines
- Generating and refactoring ETL scripts with AI
- Structuring feature pipelines for scalability
- Version-controlling 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 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 model lifecycle and version tracking
AI-Assisted Documentation and Reporting
- Generating inline documentation for data pipelines
- Creating experiment summaries and progress reports
- Improving team collaboration with 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 maintaining traceability
Optimizing 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
- Experience with Python-based data analysis or machine learning
- Understanding of ETL and model training workflows
- Familiarity with version control and data pipeline tools
Audience
- Data scientists building and iterating on ML notebooks
- Machine learning engineers designing training and inference pipelines
- MLOps professionals managing model deployment and reproducibility
14 Hours
