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Duration 14 hours
Course Outline
Introduction to Cursor in Data and ML Contexts
- Overview of Cursor’s function in data and ML engineering
- Configuring the development environment and linking data sources
- Comprehending AI-driven coding support within notebooks
Speeding Up Notebook Creation
- Creating and organizing Jupyter notebooks inside Cursor
- Applying AI for code completion, data exploration, and visual representation
- Recording experiments to ensure reproducible results
Constructing ETL and Feature Engineering Pipelines
- Generating and refactoring ETL scripts using AI
- Designing feature pipelines for scalability
- Managing version control for pipeline elements and datasets
Model Training and Evaluation via Cursor
- Building the foundation for model training code and evaluation cycles
- Incorporating data preprocessing and hyperparameter tuning
- Guaranteeing 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-Supported Documentation and Reporting
- Generating inline documentation for data pipelines
- Producing experiment summaries and progress updates
- Enhancing team collaboration through context-linked documentation
Reproducibility and Governance in ML Projects
- Applying best practices for data and model lineage
- Maintaining governance and compliance standards with AI-generated code
- Auditing AI decisions to preserve traceability
Enhancing Productivity and Future Applications
- Using prompt strategies to accelerate iteration
- Identifying automation opportunities in data operations
- Getting ready for upcoming advancements in Cursor and ML integration
Summary and Next Steps
Requirements
- Proficiency in Python-based data analysis or machine learning
- Knowledge of ETL processes and model training cycles
- Awareness of version control systems and data pipeline utilities
Target Audience
- Data scientists focused on creating and refining ML notebooks
- Machine learning engineers designing training and inference architectures
- MLOps specialists overseeing model deployment and reproducibility
