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Duration 14 hours
Course Outline
Introduction to Cursor for Data and ML Workflows
- An overview of Cursor’s strategic role in data and ML engineering
- Configuring the environment and establishing connections to data sources
- Gaining an understanding of AI-powered code assistance within notebooks
Accelerating Notebook Development
- Creating and managing Jupyter notebooks directly within Cursor
- Leveraging AI for efficient code completion, data exploration, and visualization
- Documenting experiments to maintain high standards 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
- Building scaffolds for model training code and evaluation loops
- Integrating data preprocessing and hyperparameter tuning seamlessly
- Ensuring 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 the model lifecycle and tracking versions effectively
AI-Assisted Documentation and Reporting
- Generating inline documentation for data pipelines automatically
- Creating concise experiment summaries and progress reports
- Enhancing team collaboration through context-linked documentation
Reproducibility and Governance in ML Projects
- Implementing best practices for tracking data and model lineage
- Maintaining governance and compliance standards when using AI-generated code
- Auditing AI decisions to ensure full traceability
Optimizing Productivity and Future Applications
- Applying strategic prompting techniques for faster iteration
- Identifying automation opportunities within data operations
- Preparing for future advancements in Cursor and ML integration
Summary and Next Steps
Requirements
- Hands-on experience with Python-based data analysis or machine learning tasks
- 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 ensuring reproducibility
