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Course Outline
Introduction to Cursor for Data and ML Workflows
- Overview of Cursor's function in data and ML engineering
- Configuring the environment and linking data sources
- Comprehending AI-driven code support in notebooks
Speeding Up Notebook Development
- Creation and management of Jupyter notebooks within Cursor
- Applying AI for code completion, data discovery, and visualization
- Documenting experiments to ensure reproducibility
Constructing ETL and Feature Engineering Pipelines
- Generating and reworking ETL scripts using AI
- Designing feature pipelines for scalability
- Managing version control for pipeline components and datasets
Model Training and Evaluation using Cursor
- Creating scaffolds for model training code and evaluation cycles
- Incorporating data preprocessing and hyperparameter optimization
- Ensuring model reproducibility across different environments
Integrating Cursor into MLOps Pipelines
- Linking Cursor with 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
- Drafting experiment summaries and progress updates
- Enhancing team collaboration via context-linked documentation
Reproducibility and Governance in ML Projects
- Adopting best practices for data and model lineage
- Upholding governance and compliance standards with AI-generated code
- Auditing AI decisions to maintain traceability
Optimizing Productivity and Future Applications
- Implementing prompt strategies for quicker iteration
- Investigating automation potential in data operations
- Preparing for upcoming advancements in Cursor and ML integration
Summary and Next Steps
Requirements
- Practical experience in Python-based data analysis or machine learning
- Knowledge of ETL processes and model training workflows
- Proficiency with version control systems and data pipeline utilities
Target Audience
- Data scientists creating and refining ML notebooks
- Machine learning engineers architecting training and inference pipelines
- MLOps specialists overseeing model deployment and reproducibility
14 Hours