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
- Understanding Cursor’s role in data and ML engineering
- Setting up the environment and establishing connections to data sources
- Exploring AI-powered code assistance features within notebooks
Enhancing Notebook Development
- Creating and managing Jupyter notebooks inside Cursor
- Leveraging AI for code completion, data exploration, and visualization tasks
- Documenting experiments to ensure reproducibility
Developing ETL and Feature Engineering Pipelines
- Generating and refactoring ETL scripts with AI support
- Designing feature pipelines that are built for scalability
- Implementing version control for pipeline components and datasets
Model Training and Evaluation using Cursor
- Structuring code for model training and evaluation loops
- Incorporating data preprocessing and hyperparameter tuning processes
- Guaranteeing model reproducibility across different environments
Integrating Cursor into MLOps Pipelines
- Linking Cursor to model registries and CI/CD workflows
- Utilizing AI-assisted scripts for automated retraining and deployment tasks
- Tracking the model lifecycle and managing versions effectively
AI-Driven Documentation and Reporting
- Generating inline documentation for data pipelines
- Producing concise experiment summaries and progress reports
- Fostering better team collaboration through context-linked documentation
Ensuring Reproducibility and Governance in ML Projects
- Adopting best practices for data and model lineage tracking
- Maintaining governance and compliance standards for AI-generated code
- Auditing AI-driven decisions to ensure full traceability
Optimizing Productivity and Exploring Future Applications
- Employing effective prompting strategies to accelerate iteration cycles
- Identifying automation opportunities within data operations
- Preparing for upcoming advancements in Cursor and ML integrations
Summary and Next Steps
Requirements
- Hands-on experience with Python-based data analysis or machine learning tasks
- A solid grasp of ETL processes and model training workflows
- Knowledge of version control systems and data pipeline tools
Target Audience
- Data scientists focused on building and refining ML notebooks
- Machine learning engineers responsible for designing training and inference pipelines
- MLOps professionals overseeing model deployment and ensuring reproducibility
14 Hours
