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Course Outline
Introduction to Cursor in Data and ML Workflows
- Examining Cursor's function within data and ML engineering practices
- Configuring the environment and establishing connections to data sources
- Gaining an understanding of AI-based code assistance within notebooks
Expediting Notebook Development
- Creating and managing Jupyter notebooks directly inside Cursor
- Leveraging AI for code completion, data exploration, and visualization tasks
- Recording experimental details to uphold reproducibility
Constructing ETL and Feature Engineering Pipelines
- Writing and restructuring ETL scripts with the aid of AI
- Organizing feature pipelines to support scalability
- Applying version control to pipeline components and associated datasets
Model Training and Evaluation Using Cursor
- Drafting model training code and evaluation loops
- Incorporating data preprocessing and hyperparameter tuning processes
- Securing model reproducibility across different environments
Embedding Cursor into MLOps Pipelines
- Linking Cursor with model registries and CI/CD workflows
- Employing AI-assisted scripts for automated retraining and deployment tasks
- Monitoring the model lifecycle and maintaining version tracking
AI-Assisted Documentation and Reporting
- Generating inline documentation for data pipelines
- Drafting experiment summaries and progress reports
- Enhancing team collaboration through context-connected documentation
Reproducibility and Governance in ML Projects
- Adopting best practices for data and model lineage tracking
- Upholding governance and compliance standards when using AI-generated code
- Auditing AI decisions and ensuring full traceability
Optimizing Productivity and Future Applications
- Implementing prompt strategies to enable faster iteration cycles
- Identifying automation opportunities within data operations
- Preparing for future advancements in Cursor and ML integrations
Conclusion and Recommended Next Steps
Requirements
- Practical experience with Python-centric data analysis or machine learning tasks
- A solid grasp of ETL processes and model training workflows
- Proficiency with version control systems and data pipeline utilities
Target Audience
- Data scientists focused on building and refining ML notebooks
- Machine learning engineers responsible for designing training and inference pipelines
- MLOps specialists overseeing model deployment and ensuring reproducibility
14 Hours
