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Duration 14 hours (2 days)
Course Outline
Introduction to Cursor for Data and ML Workflows
- An overview of Cursor’s place in data and ML engineering.
- Setting up the development environment and linking data sources.
- Grasping how AI-powered code assistance works in notebooks.
Speeding Up Notebook Development
- Creating and managing Jupyter notebooks inside Cursor.
- Leveraging AI for code completion, data exploration, and visualization.
- Documenting experiments and maintaining reproducibility standards.
Constructing ETL and Feature Engineering Pipelines
- Using AI to generate and refactor ETL scripts.
- Structuring feature pipelines to ensure scalability.
- Managing version control for pipeline components and datasets.
Model Training and Evaluation with Cursor
- Building the framework for model training code and evaluation loops.
- Incorporating data preprocessing and hyperparameter tuning.
- Safeguarding model reproducibility across different environments.
Integrating Cursor into MLOps Pipelines
- Linking 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.
- Producing experiment summaries and progress reports.
- Enhancing team collaboration through 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 to maintain traceability.
Optimizing Productivity and Future Applications
- Employing prompt strategies for faster iteration cycles.
- Investigating automation possibilities in data operations.
- Getting ready for future advancements in Cursor and ML integration.
Summary and Next Steps
Requirements
- Practical experience in Python-based data analysis or machine learning.
- A solid understanding of ETL processes and model training workflows.
- Familiarity with version control systems and data pipeline tools.
Target Audience
- Data scientists who are building and refining ML notebooks.
- Machine learning engineers designing pipelines for training and inference.
- MLOps professionals responsible for managing model deployment and ensuring reproducibility.
