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Duration 14 hours (2 days)
Course Outline
Introduction to Cursor for Data and ML Workflows
- An overview of Cursor’s position in data and ML engineering
- Configuring the environment and linking data sources
- Gaining insight into AI-driven code assistance within notebooks
Expediting Notebook Development
- Creating and managing Jupyter notebooks inside Cursor
- Leveraging AI for code completion, data exploration, and visualization
- Documenting experiments to uphold reproducibility
Constructing ETL and Feature Engineering Pipelines
- Producing and restructuring ETL scripts using AI
- Organizing feature pipelines for scalability
- Managing version control for pipeline components and datasets
Model Training and Evaluation using Cursor
- Building the framework for model training code and evaluation loops
- Incorporating data preprocessing and hyperparameter tuning
- Guaranteeing 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
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
- Applying best practices for data and model lineage
- Upholding governance and compliance standards with AI-generated code
- Auditing AI decisions to maintain traceability
Enhancing Productivity and Future Applications
- Implementing prompt strategies for more rapid iteration
- Investigating automation potential in data operations
- Preparing for future advancements in Cursor and ML integration
Summary and Next Steps
Requirements
- Practical experience with Python-based data analysis or machine learning
- A solid understanding of ETL and model training processes
- Comfort with version control systems and data pipeline tools
Target Audience
- Data scientists who create and refine ML notebooks
- Machine learning engineers responsible for designing training and inference pipelines
- MLOps professionals overseeing model deployment and ensuring reproducibility
