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Duration 14 hours
Course Outline
Fundamentals of Cursor for Government Data and Machine Learning Operations
- Role of Cursor in enhancing data and ML engineering capabilities for government
- Environment configuration and secure integration of data sources
- Functionality of AI-assisted coding within notebook environments
Enhancing Notebook Development Efficiency
- Creation and administration of Jupyter notebooks within the Cursor interface
- Application of AI tools for code completion, data analysis, and visual outputs
- Maintenance of experiment records and adherence to reproducibility standards
Constructing ETL and Feature Engineering Frameworks
- Automation of ETL script generation and optimization using AI
- Design of scalable feature engineering structures
- Implementation of version control protocols for pipeline assets and datasets
Model Training and Assessment Protocols
- Establishment of foundational code for model training and evaluation cycles
- Incorporation of data preprocessing and hyperparameter optimization workflows
- Ensuring consistent model reproducibility across deployment environments
Integration of Cursor into MLOps Architectures
- Linkage of Cursor with model registries and CI/CD operational flows
- Deployment of AI-assisted scripts for automated retraining and release processes
- Oversight of the model lifecycle and version management
AI-Driven Documentation and Reporting Standards
- Automation of inline documentation for data processing pipelines
- Generation of experimental summaries and progress reporting documents
- Facilitation of inter-agency or team collaboration through contextually linked documentation
Reproducibility and Governance in Government ML Initiatives
- Adoption of best practices for data and model lineage tracking
- Maintenance of governance and compliance frameworks for AI-generated code for government
- Auditability of AI decision processes and preservation of traceability records
Productivity Optimization and Strategic Outlook
- Implementation of prompting strategies to accelerate iterative development
- Exploration of automation potentials within data operational frameworks
- Preparation for future advancements in Cursor and ML integration for government use
Concluding Summary and Action Plan
Requirements
- Proficiency in Python-based data analysis or machine learning
- Working knowledge of ETL and model training workflows
- Familiarity with version control systems and data pipeline tools
Target Audience
- Data scientists responsible for developing and iterating ML notebooks for government
- Machine learning engineers designing training and inference pipelines
- MLOps professionals overseeing model deployment and reproducibility standards
