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Course Outline
Introduction to Cursor in Data and ML Workflows
- An overview of how Cursor fits into data and ML engineering practices
- Setting up the development environment and connecting various data sources
- Gaining insight into AI-powered code assistance within notebooks
Expediting Notebook Development
- Creating and managing Jupyter notebooks directly within Cursor
- Applying AI to code completion, data exploration, and visualization tasks
- Documenting experiments effectively to maintain reproducibility
Constructing ETL and Feature Engineering Pipelines
- Generating and refining ETL scripts with AI assistance
- Designing feature pipelines that are built for scalability
- Managing version control for pipeline components and datasets
Model Training and Evaluation Using Cursor
- Scaffolding code for model training and setting up evaluation loops
- Integrating data preprocessing steps with hyperparameter tuning
- Ensuring model reproducibility across different environments
Integrating Cursor into MLOps Pipelines
- Linking Cursor to model registries and CI/CD workflows
- Employing AI-assisted scripts for automated retraining and deployment
- Monitoring the model lifecycle and tracking versions effectively
AI-Assisted Documentation and Reporting
- Automating inline documentation for data pipelines
- Drafting experiment summaries and progress reports with AI support
- Enhancing team collaboration through context-linked documentation
Reproducibility and Governance in ML Projects
- Applying best practices for tracking data and model lineage
- Upholding governance and compliance standards with AI-generated code
- Auditing AI decisions to maintain full traceability
Optimizing Productivity and Exploring Future Applications
- Implementing prompt strategies to enable faster iteration cycles
- 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 processes and model training workflows
- Comfort with version control systems and data pipeline tools
Target Audience
- Data scientists focused on building and refining ML notebooks
- Machine learning engineers designing robust training and inference pipelines
- MLOps professionals responsible for model deployment and ensuring reproducibility
14 Hours
