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Course Outline
Getting Started with Cursor for Data and ML Tasks
- An overview of Cursor’s impact on data and ML engineering
- Environment setup and establishing connections to data sources
- Comprehending AI-driven code support within notebooks
Speeding Up Notebook Creation
- Building and overseeing Jupyter notebooks inside Cursor
- Leveraging AI for code completion, data analysis, and visualization
- Documenting trials and upholding reproducibility
Constructing ETL and Feature Engineering Pipelines
- Generating and refactoring ETL scripts using AI
- Organizing feature pipelines for scalability
- Managing version control for pipeline elements and datasets
Model Training and Assessment with Cursor
- Building scaffolds for model training code and evaluation cycles
- Integrating data preprocessing and hyperparameter tuning
- Guaranteeing model reproducibility across various environments
Embedding 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-Supported Documentation and Reporting
- Producing inline documentation for data pipelines
- Generating experiment summaries and progress updates
- Enhancing team collaboration via context-linked documentation
Reproducibility and Governance in ML Initiatives
- Applying best practices for data and model lineage
- Maintaining governance and compliance standards with AI-generated code
- Auditing AI decisions and preserving traceability
Optimizing Efficiency and Future Prospects
- Applying prompting strategies for quicker iteration
- Investigating automation possibilities in data operations
- Preparing for upcoming advancements in Cursor and ML integration
Conclusion and Path Forward
Requirements
- Hands-on experience with Python-based data analysis or machine learning
- A solid grasp of ETL and model training procedures
- Knowledge of version control systems and data pipeline tools
Target Audience
- Data scientists constructing and refining ML notebooks
- Machine learning engineers architecting training and inference pipelines
- MLOps specialists overseeing model deployment and reproducibility
14 Hours
