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Course Outline
Introduction to Cursor for Data and ML Workflows
- The role of Cursor in data and ML engineering
- Environment setup and connecting data sources
- How AI-powered code assistance functions within notebooks
Speeding Up Notebook Development
- Creating and managing Jupyter notebooks inside Cursor
- Leveraging AI for code completion, data exploration, and visualisation
- Documenting experiments to maintain reproducibility
Constructing ETL and Feature Engineering Pipelines
- Generating and refactoring ETL scripts with AI assistance
- Designing feature pipelines for scalability
- Implementing version control for pipeline components and datasets
Model Training and Evaluation using Cursor
- Setting up model training code and evaluation loops
- Integrating data preprocessing and hyperparameter tuning
- Ensuring model reproducibility across different environments
Integrating Cursor into MLOps Pipelines
- Linking Cursor to model registries and CI/CD workflows
- Using 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 reports
- Enhancing team collaboration through context-linked documentation
Reproducibility and Governance in ML Projects
- Applying best practices for data and model lineage
- Maintaining governance and compliance with AI-generated code
- Auditing AI decisions and ensuring traceability
Optimising Productivity and Future Applications
- Using prompt strategies to accelerate iteration
- Identifying automation opportunities in data operations
- Preparing for upcoming advancements in Cursor and ML integration
Summary and Next Steps
Requirements
- Hands-on experience with Python for data analysis or machine learning
- A solid understanding of ETL processes and model training workflows
- Basic familiarity with version control systems and data pipeline tools
Target Audience
- Data scientists who build and refine ML notebooks
- Machine learning engineers responsible for designing training and inference pipelines
- MLOps professionals overseeing model deployment and ensuring reproducibility
14 Hours
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 2600 € + VAT*
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