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Duration 14 hours
Course Outline
Introduction to Cursor for Data and ML Workflows
- An overview of Cursor’s function in data and ML engineering.
- Environment setup and integration with various data sources.
- Gaining insight into AI-powered code assistance within notebooks.
Accelerating Notebook Development
- Creating and managing Jupyter notebooks within the Cursor interface.
- Leveraging AI for code completion, data exploration, and visualization tasks.
- Documenting experiments and ensuring reproducibility.
Building ETL and Feature Engineering Pipelines
- Generating and refining ETL scripts with the aid of AI.
- Designing feature pipelines with a focus on scalability.
- Applying version control to pipeline components and datasets.
Model Training and Evaluation with Cursor
- Setting up scaffolding for model training code and evaluation loops.
- Incorporating data preprocessing and hyperparameter tuning.
- Guaranteeing model reproducibility across different environments.
Integrating Cursor into MLOps Pipelines
- Connecting Cursor to model registries and CI/CD workflows.
- Employing 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.
- Fostering team collaboration through context-linked documentation.
Reproducibility and Governance in ML Projects
- Implementing best practices for data and model lineage.
- Maintaining governance and compliance standards with AI-generated code.
- Auditing AI decisions and ensuring traceability.
Optimizing Productivity and Future Applications
- Applying prompt strategies to speed up iteration cycles.
- Identifying automation opportunities within data operations.
- Preparing 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 and model training workflows.
- Familiarity with version control systems and data pipeline tools.
Target Audience
- Data scientists focused on building and refining ML notebooks.
- Machine learning engineers responsible for designing training and inference pipelines.
- MLOps professionals overseeing model deployment and reproducibility.
