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Course Outline
Introduction to Cursor in Data and ML Workflows
- The function of Cursor within data and ML engineering
- Configuring the environment and linking data sources
- Gaining insight into AI-driven code support in notebooks
Speeding Up Notebook Development
- Establishing and overseeing Jupyter notebooks inside Cursor
- Employing AI for code completion, data analysis, and visualization
- Recording experiments and preserving reproducibility
Constructing ETL and Feature Engineering Pipelines
- Creating and refining ETL scripts using AI
- Organizing feature pipelines for scalability
- Managing version control for pipeline elements and datasets
Model Training and Evaluation Using Cursor
- Drafting model training code and evaluation loops
- Incorporating data preprocessing and hyperparameter tuning
- Guaranteeing model reproducibility across various environments
Incorporating Cursor into MLOps Pipelines
- Linking Cursor to model registries and CI/CD workflows
- Using AI-assisted scripts for automated retraining and deployment
- Monitoring the model lifecycle and tracking versions
AI-Supported Documentation and Reporting
- Generating inline documentation for data pipelines
- Producing 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
- Upholding governance and compliance with AI-generated code
- Reviewing AI decisions and ensuring traceability
Maximizing Productivity and Future Uses
- Applying prompt strategies for quicker iteration
- Investigating automation possibilities in data operations
- Getting ready for future advancements in Cursor and ML integration
Summary and Next Steps
Requirements
- Background in Python-driven data analysis or machine learning
- Comprehension of ETL and model training processes
- Knowledge of version control systems and data pipeline tools
Intended Audience
- Data scientists developing and refining ML notebooks
- Machine learning engineers architecting training and inference pipelines
- MLOps specialists overseeing model deployment and reproducibility
14 Hours
