Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction to Cursor for Data and ML Workflows
- An overview of Cursor’s significance in data and ML engineering
- Configuring the development environment and establishing data source connections
- Comprehending AI-driven code assistance within notebooks
Expediting Notebook Development
- Developing and managing Jupyter notebooks inside Cursor
- Utilizing AI for code completion, data exploration, and visualization tasks
- Documenting experiments and ensuring reproducibility
Constructing ETL and Feature Engineering Pipelines
- Generating and refactoring ETL scripts with AI support
- Designing feature pipelines with a focus on scalability
- Implementing version control for pipeline components and associated datasets
Model Training and Evaluation using Cursor
- Creating foundational structures for model training code and evaluation loops
- Incorporating data preprocessing and hyperparameter tuning processes
- Safeguarding model reproducibility across various environments
Integrating Cursor into MLOps Ecosystems
- Linking Cursor with model registries and CI/CD workflows
- Employing AI-assisted scripts for automated retraining and deployment procedures
- Monitoring the model lifecycle and tracking versions effectively
AI-Supported Documentation and Reporting
- Producing inline documentation for data pipelines
- Drafting experiment summaries and progress reports
- Enhancing team collaboration through context-linked documentation
Reproducibility and Governance in ML Projects
- Adopting best practices for tracking data and model lineage
- Maintaining governance and compliance standards for AI-generated code
- Auditing AI decision-making processes and ensuring traceability
Optimizing Productivity and Future Applications
- Applying effective prompt strategies for faster iteration cycles
- Exploring automation potential within data operations
- Preparing for upcoming advancements in Cursor and ML integration
Conclusion and Next Steps
Requirements
- Practical experience with Python-based data analysis or machine learning tasks.
- A solid grasp of ETL and model training processes.
- Working knowledge of version control systems and data pipeline utilities.
Target Audience
- Data scientists focused on developing and refining ML notebooks.
- Machine learning engineers responsible for designing training and inference pipelines.
- MLOps experts overseeing model deployment and ensuring reproducibility.
14 Hours
