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Duration 14 hours (2 days)
Course Outline
Introduction to Cursor for Data and ML Workflows
- An overview of Cursor’s impact on data and ML engineering
- Setting up the environment and connecting data sources
- Gaining insight into AI-powered code assistance within notebooks
Accelerating Notebook Development
- Creating and managing Jupyter notebooks within the Cursor environment
- Leveraging AI for code completion, data exploration, and visualization tasks
- Documenting experiments to maintain reproducibility
Building ETL and Feature Engineering Pipelines
- Generating and refactoring ETL scripts with AI assistance
- Designing feature pipelines with scalability in mind
- Applying version control to pipeline components and datasets
Model Training and Evaluation with Cursor
- Structuring model training code and evaluation loops
- Integrating data preprocessing and hyperparameter tuning workflows
- Safeguarding model reproducibility across different environments
Integrating Cursor into MLOps Pipelines
- Connecting Cursor to model registries and CI/CD workflows
- Employing 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
- Producing 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 standards for AI-generated code
- Auditing AI decisions and ensuring traceability
Optimizing Productivity and Future Applications
- Applying effective prompt strategies to speed up iteration cycles
- Identifying automation opportunities within data operations
- Preparing for upcoming advancements in Cursor and ML integration
Summary and Next Steps
Requirements
- Proficiency in Python-based data analysis or machine learning
- Comprehension of ETL and model training workflows
- Knowledge of version control systems and data pipeline tools
Audience
- Data scientists focused on building and iterating ML notebooks
- Machine learning engineers designing training and inference pipelines
- MLOps professionals responsible for model deployment and reproducibility
