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Course Outline
Introduction to Cursor for Data and ML Workflows
- The role of Cursor in modern data and ML engineering.
- Environment setup and connecting data sources.
- Understanding AI-powered code assistance within notebooks.
Accelerating Notebook Development
- Creating and managing Jupyter notebooks inside Cursor.
- Leveraging AI for code completion, data exploration, and visualization.
- Documenting experiments to maintain reproducibility.
Building ETL and Feature Engineering Pipelines
- Generating and refactoring ETL scripts using AI tools.
- Structuring feature pipelines for optimal scalability.
- Managing version control for pipeline components and datasets.
Model Training and Evaluation with Cursor
- Scaffolding code for model training and evaluation loops.
- Integrating data preprocessing and hyperparameter tuning processes.
- 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 the model lifecycle and tracking versions.
AI-Assisted Documentation and Reporting
- Generating inline documentation for data pipelines.
- Creating concise 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 when using AI-generated code.
- Auditing AI decisions to ensure traceability.
Optimizing Productivity and Future Applications
- Applying prompt strategies to enable faster iteration.
- Exploring opportunities for automation in data operations.
- Preparing for future advancements in Cursor and ML integrations.
Summary and Next Steps
Requirements
- Proficiency in Python-based data analysis or machine learning.
- A solid understanding of ETL and model training workflows.
- Familiarity with version control systems and data pipeline tools.
Target Audience
- Data scientists responsible for building and iterating on ML notebooks.
- Machine learning engineers designing training and inference pipelines.
- MLOps professionals overseeing model deployment and ensuring reproducibility.
14 Hours
