Course Outline
Introduction to Cursor for Data and ML Workflows <\/p>
- Overview of Cursor’s role in data and ML engineering <\/li>
- Setting up the environment and connecting data sources <\/li>
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Understanding AI-powered code assistance in notebooks
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Accelerating Notebook Development <\/p>
- Creating and managing Jupyter notebooks within Cursor <\/li>
- Using AI for code completion, data exploration, and visualization <\/li>
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Documenting experiments and maintaining reproducibility
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Building ETL and Feature Engineering Pipelines <\/p>
- Generating and refactoring ETL scripts with AI <\/li>
- Structuring feature pipelines for scalability <\/li>
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Version-controlling pipeline components and datasets
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Model Training and Evaluation with Cursor <\/p>
- Scaffolding model training code and evaluation loops <\/li>
- Integrating data preprocessing and hyperparameter tuning <\/li>
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Ensuring model reproducibility across environments
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Integrating Cursor into MLOps Pipelines <\/p>
- Connecting Cursor to model registries and CI/CD workflows <\/li>
- Using AI-assisted scripts for automated retraining and deployment <\/li>
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Monitoring model lifecycle and version tracking
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AI-Assisted Documentation and Reporting <\/p>
- Generating inline documentation for data pipelines <\/li>
- Creating experiment summaries and progress reports <\/li>
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Improving team collaboration with context-linked documentation
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Reproducibility and Governance in ML Projects <\/p>
- Implementing best practices for data and model lineage <\/li>
- Maintaining governance and compliance with AI-generated code <\/li>
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Auditing AI decisions and maintaining traceability
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Optimizing Productivity and Future Applications <\/p>
- Applying prompt strategies for faster iteration <\/li>
- Exploring automation opportunities in data operations <\/li>
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Preparing for future Cursor and ML integration advancements
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Summary and Next Steps <\/p>
Requirements
- Experience with Python-based data analysis or machine learning <\/li>
- Understanding of ETL and model training workflows <\/li>
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Familiarity with version control and data pipeline tools
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Audience<\/strong> <\/p>
- Data scientists building and iterating on ML notebooks <\/li>
- Machine learning engineers designing training and inference pipelines <\/li>
- MLOps professionals managing model deployment and reproducibility <\/li> <\/ul>
