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Course Outline
Introduction to Cursor for Data and ML Workflows
- Examining Cursor's function in data and ML engineering
- Configuring the environment and establishing data source connections
- Exploring AI-powered code assistance features in notebooks
Expediting Notebook Development
- Creating and managing Jupyter notebooks within the Cursor interface
- Applying AI tools for code completion, data analysis, and visualization
- Documenting experiments to uphold reproducibility standards
Constructing ETL and Feature Engineering Pipelines
- Generating and restructuring ETL scripts with AI support
- Architecting feature pipelines for long-term scalability
- Managing version control for pipeline components and datasets
Model Training and Evaluation Using Cursor
- Building the structure for model training code and evaluation cycles
- Incorporating data preprocessing and hyperparameter tuning workflows
- Safeguarding model reproducibility across diverse environments
Integrating Cursor into MLOps Pipelines
- Linking Cursor with model registries and CI/CD processes
- Deploying AI-assisted scripts for automated retraining and release
- Overseeing model lifecycle management and version history
AI-Assisted Documentation and Reporting
- Producing inline documentation for data pipelines
- Drafting experiment summaries and progress reports
- Enhancing team cooperation through context-aware documentation
Reproducibility and Governance in ML Projects
- Adopting best practices for data and model lineage tracking
- Maintaining governance and compliance regarding AI-generated code
- Auditing AI-driven decisions to ensure full traceability
Optimizing Productivity and Future Applications
- Employing 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 and data pipeline tools
Audience
- Data scientists developing and refining ML notebooks
- Machine learning engineers designing training and inference pipelines
- MLOps professionals overseeing model deployment and reproducibility
14 Hours
