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Course Outline
Introduction to Cursor for Data and ML Workflows
- Overview of Cursor’s role in data and ML engineering.
- Setting up the development environment and connecting data sources.
- Understanding AI-powered code assistance within notebooks.
Accelerating Notebook Development
- Creating and managing Jupyter notebooks within Cursor.
- Leveraging AI for code completion, data exploration, and visualization.
- Documenting experiments and maintaining reproducibility standards.
Building ETL and Feature Engineering Pipelines
- Generating and refactoring ETL scripts using AI assistance.
- Structuring feature pipelines for scalability.
- Applying version control to pipeline components and datasets.
Model Training and Evaluation with Cursor
- Scaffolding model training code and evaluation loops.
- Integrating data preprocessing steps and hyperparameter tuning.
- Ensuring model reproducibility across different environments.
Integrating Cursor into MLOps Pipelines
- Connecting Cursor to model registries and CI/CD workflows.
- Utilizing AI-assisted scripts for automated retraining and deployment.
- Monitoring model lifecycle and tracking versions.
AI-Assisted Documentation and Reporting
- Generating inline documentation for data pipelines.
- Creating experiment summaries and progress reports.
- Enhancing team collaboration through context-linked documentation.
Reproducibility and Governance in ML Projects
- Implementing best practices for data and model lineage.
- Maintaining governance and compliance with AI-generated code.
- Auditing AI decisions and maintaining traceability.
Optimizing Productivity and Future Applications
- Applying prompt strategies for faster iteration cycles.
- Exploring automation opportunities in data operations.
- Preparing for future advancements in Cursor and ML integration.
Summary and Next Steps
Requirements
- Experience with Python-based data analysis or machine learning projects.
- A solid understanding of ETL processes and model training workflows.
- Familiarity with version control systems and data pipeline tools.
Audience
- Data scientists who build and iterate on ML notebooks.
- Machine learning engineers responsible for designing training and inference pipelines.
- MLOps professionals managing model deployment and ensuring reproducibility.
14 Hours
