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Duration 14 hours (2 days)
Course Outline
Introduction to Cursor for Data and ML Workflows
- The role of Cursor in data and ML engineering
- Configuring the development environment and linking data sources
- Comprehending AI-powered code assistance within notebooks
Expediting Notebook Development
- Creating and managing Jupyter notebooks using Cursor
- Leveraging AI for code completion, data exploration, and visualization tasks
- Documenting experimental processes to ensure reproducibility
Constructing ETL and Feature Engineering Pipelines
- Generating and refactoring ETL scripts with AI support
- Organizing feature pipelines to ensure scalability
- Implementing 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
- Safeguarding model reproducibility across different environments
Incorporating 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
- Producing inline documentation for data pipelines
- Generating experiment summaries and progress reports
- Enhancing team collaboration through context-linked documentation
Ensuring Reproducibility and Governance in ML Projects
- Applying best practices for data and model lineage management
- Upholding governance and compliance standards with AI-generated code
- Auditing AI-driven decisions and maintaining full traceability
Maximizing Productivity and Exploring Future Applications
- Utilizing effective prompt strategies to accelerate iteration cycles
- Identifying automation opportunities within data operations
- Preparing for future advancements in the integration of Cursor and ML
Conclusion and Subsequent Steps
Requirements
- Hands-on experience with 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 focused on building and iterating on ML notebooks.
- Machine learning engineers responsible for designing training and inference pipelines.
- MLOps professionals tasked with managing model deployment and ensuring reproducibility.
