Machine Learning in Production

8.

Governance, Ethics, and Security

8.1.

Model Governance Framework

8.1.1.

Governance Objectives

8.1.2.

Stakeholder Roles and Responsibilities

8.1.3.

Decision-making Processes

8.2.

Model Governance and Compliance

8.2.1.

Auditing and Traceability

8.2.1.1.

Audit Trails for Data and Models

8.2.1.2.

Change Management

8.2.1.3.

Documentation Standards

8.2.2.

Regulatory Requirements

8.2.2.1.

GDPR Compliance

8.2.2.1.1.

Data Protection Requirements

8.2.2.1.2.

Right to Explanation

8.2.2.1.3.

Data Minimization

8.2.2.2.

CCPA Compliance

8.2.2.2.1.

Consumer Rights

8.2.2.2.2.

Data Transparency

8.2.2.3.

Industry-specific Regulations

8.2.2.3.1.

Financial Services

8.2.2.3.2.

8.2.2.3.3.

8.2.3.

Model Risk Management

8.2.3.1.

Risk Assessment Frameworks

8.2.3.2.

Risk Mitigation Strategies

8.2.3.3.

Risk Monitoring

8.2.4.

Model Validation

8.2.4.1.

Independent Validation

8.2.4.2.

Validation Documentation

8.2.4.3.

Ongoing Validation

8.3.

Responsible AI in Production

8.3.1.

Fairness and Bias

8.3.1.1.

Bias Sources and Types

8.3.1.2.

Bias Detection Methods

8.3.1.3.

Bias Mitigation Techniques

8.3.1.4.

Fairness Metrics

8.3.2.

Accountability and Transparency

8.3.2.1.

Model Documentation

8.3.2.2.

Decision Audit Trails

8.3.2.3.

Stakeholder Communication

8.3.3.

Explainability Requirements

8.3.3.1.

Regulatory Explainability

8.3.3.2.

Business Explainability

8.3.3.3.

Technical Explainability

8.3.4.

Privacy-Preserving ML Techniques

8.3.4.1.

Differential Privacy

8.3.4.1.1.

Privacy Budget Management

8.3.4.1.2.

Noise Addition Techniques

8.3.4.1.3.

Privacy-Utility Tradeoffs

8.3.4.2.

Federated Learning

8.3.4.2.1.

Distributed Training

8.3.4.2.2.

Privacy Preservation

8.3.4.2.3.

Communication Efficiency

8.3.4.3.

Data Anonymization

8.3.4.3.1.

Anonymization Techniques

8.3.4.3.2.

Re-identification Risks

8.3.4.3.3.

Utility Preservation

8.3.5.

Ethical AI Frameworks

8.3.5.1.

Ethical Guidelines

8.3.5.2.

Ethics Review Processes

8.3.5.3.

Stakeholder Engagement

8.4.

Security for ML Systems

8.4.1.

Threat Modeling for ML Systems

8.4.1.1.

8.4.1.2.

Threat Assessment

8.4.1.3.

Security Requirements

8.4.2.

Securing ML Pipelines

8.4.2.1.

8.4.2.1.1.

Role-based Access Control

8.4.2.1.2.

Attribute-based Access Control

8.4.2.1.3.

Multi-factor Authentication

8.4.2.2.

Secure Code Practices

8.4.2.2.1.

Code Review Processes

8.4.2.2.2.

Static Code Analysis

8.4.2.2.3.

Dependency Scanning

8.4.2.3.

Pipeline Security

8.4.2.3.1.

Secure Communication

8.4.2.3.2.

Credential Management

8.4.2.3.3.

8.4.3.

8.4.3.1.

Adversarial Attack Protection

8.4.3.1.1.

Adversarial Examples

8.4.3.1.2.

Model Poisoning

8.4.3.1.3.

Evasion Attacks

8.4.3.2.

Model Hardening Techniques

8.4.3.2.1.

Adversarial Training

8.4.3.2.2.

Input Validation

8.4.3.2.3.

Output Sanitization

8.4.3.3.

Model Intellectual Property Protection

8.4.3.3.1.

Model Watermarking

8.4.3.3.2.

Model Obfuscation

8.4.3.3.3.

Access Controls

8.4.4.

8.4.4.1.

Data Encryption

8.4.4.1.1.

Encryption at Rest

8.4.4.1.2.

Encryption in Transit

8.4.4.1.3.

8.4.4.2.

Data Access Controls

8.4.4.2.1.

Data Classification

8.4.4.2.2.

Access Policies

8.4.4.2.3.

8.4.4.3.

8.4.4.3.1.

Personal Data Protection

8.4.4.3.2.

Data Retention Policies

8.4.4.3.3.

8.4.5.

Infrastructure Security

8.4.5.1.

Network Security

8.4.5.1.1.

Firewalls and VPNs

8.4.5.1.2.

Network Segmentation

8.4.5.1.3.

Intrusion Detection

8.4.5.2.

Container Security

8.4.5.2.1.

8.4.5.2.2.

Runtime Security

8.4.5.2.3.

Secrets Management

8.4.5.3.

8.4.5.3.1.

Identity and Access Management

8.4.5.3.2.

Resource Policies

8.4.5.3.3.

Security Monitoring

8.4.6.

Incident Response and Recovery

8.4.6.1.

Security Incident Response

8.4.6.1.1.

Incident Detection

8.4.6.1.2.

Response Procedures

8.4.6.1.3.

Recovery Planning

8.4.6.2.

Business Continuity

8.4.6.2.1.

Backup and Recovery

8.4.6.2.2.

Disaster Recovery

8.4.6.2.3.

Service Continuity

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7. MLOps Infrastructure and Tooling

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1. Introduction to MLOps