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
Introductory Session: Cursor for Data and ML Workflows
- An overview of Cursor’s function in data and ML engineering
- Environment setup and connection to data sources
- Comprehending AI-driven code assistance within notebooks
Expediting Notebook Development
- Creating and managing Jupyter notebooks inside Cursor
- Leveraging AI for code completion, data exploration, and visualization
- Recording experiments and ensuring reproducibility
Constructing ETL and Feature Engineering Pipelines
- Generating and refactoring ETL scripts with AI support
- Designing feature pipelines for scalability
- Managing version control for pipeline components and datasets
Model Training and Evaluation Using Cursor
- Setting up model training code and evaluation loops
- Incorporating data preprocessing and hyperparameter tuning
- Guaranteeing model reproducibility across different environments
Embedding Cursor into MLOps Pipelines
- Linking 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-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
- Applying best practices for data and model lineage
- Upholding governance and compliance with AI-generated code
- Auditing AI decisions and preserving traceability
Enhancing Productivity and Future Outlooks
- Implementing prompt strategies for quicker iteration
- Identifying automation opportunities in data operations
- Preparing for future advancements in Cursor and ML integration
Conclusion and Next Steps
Requirements
- Hands-on experience with Python-based data analysis or machine learning
- Knowledge of ETL and model training workflows
- Proficiency with version control and data pipeline tools
Target Audience
- Data scientists focused on building and refining ML notebooks
- Machine learning engineers designing training and inference pipelines
- MLOps specialists responsible for model deployment and ensuring reproducibility
14 Hours
