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Computer Science
Artificial Intelligence
Machine Learning
Machine Learning in Production
1. Introduction to MLOps
2. Project Scoping and System Design
3. Data Engineering for Production
4. Model Development for Production
5. Model Deployment
6. Monitoring, Logging, and Maintenance
7. MLOps Infrastructure and Tooling
8. Governance, Ethics, and Security
Governance, Ethics, and Security
Model Governance Framework
Governance Objectives
Stakeholder Roles and Responsibilities
Decision-making Processes
Model Governance and Compliance
Auditing and Traceability
Audit Trails for Data and Models
Change Management
Documentation Standards
Regulatory Requirements
GDPR Compliance
Data Protection Requirements
Right to Explanation
Data Minimization
CCPA Compliance
Consumer Rights
Data Transparency
Industry-specific Regulations
Financial Services
Healthcare
Automotive
Model Risk Management
Risk Assessment Frameworks
Risk Mitigation Strategies
Risk Monitoring
Model Validation
Independent Validation
Validation Documentation
Ongoing Validation
Responsible AI in Production
Fairness and Bias
Bias Sources and Types
Bias Detection Methods
Bias Mitigation Techniques
Fairness Metrics
Accountability and Transparency
Model Documentation
Decision Audit Trails
Stakeholder Communication
Explainability Requirements
Regulatory Explainability
Business Explainability
Technical Explainability
Privacy-Preserving ML Techniques
Differential Privacy
Privacy Budget Management
Noise Addition Techniques
Privacy-Utility Tradeoffs
Federated Learning
Distributed Training
Privacy Preservation
Communication Efficiency
Data Anonymization
Anonymization Techniques
Re-identification Risks
Utility Preservation
Ethical AI Frameworks
Ethical Guidelines
Ethics Review Processes
Stakeholder Engagement
Security for ML Systems
Threat Modeling for ML Systems
Attack Vectors
Threat Assessment
Security Requirements
Securing ML Pipelines
Access Control
Role-based Access Control
Attribute-based Access Control
Multi-factor Authentication
Secure Code Practices
Code Review Processes
Static Code Analysis
Dependency Scanning
Pipeline Security
Secure Communication
Credential Management
Audit Logging
Model Security
Adversarial Attack Protection
Adversarial Examples
Model Poisoning
Evasion Attacks
Model Hardening Techniques
Adversarial Training
Input Validation
Output Sanitization
Model Intellectual Property Protection
Model Watermarking
Model Obfuscation
Access Controls
Data Security
Data Encryption
Encryption at Rest
Encryption in Transit
Key Management
Data Access Controls
Data Classification
Access Policies
Data Masking
Data Privacy
Personal Data Protection
Data Retention Policies
Data Deletion
Infrastructure Security
Network Security
Firewalls and VPNs
Network Segmentation
Intrusion Detection
Container Security
Image Scanning
Runtime Security
Secrets Management
Cloud Security
Identity and Access Management
Resource Policies
Security Monitoring
Incident Response and Recovery
Security Incident Response
Incident Detection
Response Procedures
Recovery Planning
Business Continuity
Backup and Recovery
Disaster Recovery
Service Continuity
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7. MLOps Infrastructure and Tooling
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1. Introduction to MLOps