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Course Outline
Introduction to Cursor for Data and ML Workflows
- Overview of Cursor’s Role in Data and Machine Learning Engineering for Government
- Setting Up the Environment and Connecting Data Sources for Government Operations
- Understanding AI-Powered Code Assistance in Notebooks for Government Projects
Accelerating Notebook Development for Government
- Creating and Managing Jupyter Notebooks within Cursor for Government Use
- Using AI for Code Completion, Data Exploration, and Visualization in Government Projects
- Documenting Experiments and Maintaining Reproducibility for Government Compliance
Building ETL and Feature Engineering Pipelines for Government
- Generating and Refactoring ETL Scripts with AI for Government Data Projects
- Structuring Feature Pipelines for Scalability in Government Operations
- Version-Controlling Pipeline Components and Datasets for Government Transparency
Model Training and Evaluation with Cursor for Government
- Scaffolding Model Training Code and Evaluation Loops for Government Models
- Integrating Data Preprocessing and Hyperparameter Tuning for Government Applications
- Ensuring Model Reproducibility Across Environments for Government Use
Integrating Cursor into MLOps Pipelines for Government
- Connecting Cursor to Model Registries and CI/CD Workflows for Government Projects
- Using AI-Assisted Scripts for Automated Retraining and Deployment in Government Systems
- Monitoring Model Lifecycle and Version Tracking for Government Compliance
AI-Assisted Documentation and Reporting for Government
- Generating Inline Documentation for Data Pipelines in Government Projects
- Creating Experiment Summaries and Progress Reports for Government Audits
- Improving Team Collaboration with Context-Linked Documentation for Government Teams
Reproducibility and Governance in ML Projects for Government
- Implementing Best Practices for Data and Model Lineage in Government Projects
- Maintaining Governance and Compliance with AI-Generated Code for Government
- Auditing AI Decisions and Maintaining Traceability for Government Accountability
Optimizing Productivity and Future Applications for Government
- Applying Prompt Strategies for Faster Iteration in Government Data Projects
- Exploring Automation Opportunities in Data Operations for Government Efficiency
- Preparing for Future Cursor and ML Integration Advancements for Government Use
Summary and Next Steps for Government
Requirements
- Experience with Python-based data analysis or machine learning for government applications
- Understanding of ETL processes and model training workflows
- Familiarity with version control systems and data pipeline tools
Audience
- Data scientists engaged in building and refining machine learning notebooks for government projects
- Machine learning engineers responsible for designing training and inference pipelines for government initiatives
- MLOps professionals tasked with managing model deployment and ensuring reproducibility for government operations
14 Hours
