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Duration 14 hours (2 days)
Course Outline
Introduction to Cursor for Data and ML Workflows
- Overview of Cursor's position in data and ML engineering
- Configuring the environment and linking data sources
- Gaining insight into AI-powered code aid within notebooks
Expediting Notebook Development
- Creating and handling Jupyter notebooks inside Cursor
- Leveraging AI for code completion, data exploration, and visualization
- Recording experiments and upholding reproducibility
Constructing ETL and Feature Engineering Pipelines
- Producing and restructuring ETL scripts using AI
- Organising feature pipelines for scalability
- Managing version control for pipeline components and datasets
Model Training and Evaluation with Cursor
- Building scaffolds for model training code and evaluation loops
- Incorporating data preprocessing and hyperparameter tuning
- Safeguarding 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
- Tracking the model lifecycle and versioning
AI-Assisted Documentation and Reporting
- Generating inline documentation for data pipelines
- Compiling experiment summaries and progress reports
- Enhancing team collaboration via context-linked documentation
Reproducibility and Governance in ML Projects
- Applying best practices for data and model lineage
- Upholding governance and compliance with AI-generated code
- Auditing AI decisions and preserving traceability
Enhancing Productivity and Future Applications
- Applying prompt strategies for quicker iteration
- Investigating automation possibilities in data operations
- Getting ready for future Cursor and ML integration advancements
Summary and Next Steps
Requirements
- Background in Python-based data analysis or machine learning
- Knowledge of ETL and model training processes
- Comfort with version control and data pipeline utilities
Target Audience
- Data scientists developing and refining ML notebooks
- Machine learning engineers creating training and inference pipelines
- MLOps specialists overseeing model deployment and reproducibility
