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Duration 14 hours
Course Outline
Introduction to Cursor for Data and ML Workflows
- Understanding Cursor’s role in data and ML engineering
- Setting up the development environment and connecting data sources
- Exploring AI-powered code assistance within notebooks
Speeding Up Notebook Development
- Creating and managing Jupyter notebooks inside Cursor
- Applying AI for code completion, data exploration, and visualization
- Documenting experiments to ensure reproducibility
Constructing ETL and Feature Engineering Pipelines
- Generating and optimizing ETL scripts with AI support
- Designing feature pipelines for scalability
- Managing version control for pipeline components and datasets
Model Training and Evaluation using Cursor
- Scaffolding code for model training and evaluation loops
- Incorporating data preprocessing and hyperparameter tuning
- Ensuring model reproducibility across different environments
Integrating Cursor into MLOps Pipelines
- Linking Cursor with model registries and CI/CD workflows
- Utilizing 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
- Drafting 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 prompt strategies for faster iteration cycles
- Exploring 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
- Working knowledge of ETL and model training workflows
- Familiarity with version control systems and data pipeline tools
Target Audience
- Data scientists developing and refining ML notebooks
- Machine learning engineers architecting training and inference pipelines
- MLOps professionals overseeing model deployment and reproducibility
