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Strategic AI Governance Specialization

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Coursera

Strategic AI Governance Specialization

Lead AI Governance and Responsible Deployment. Build expertise in AI ethics, governance frameworks, and operational excellence for enterprises.

Caio Avelino
Starweaver
Karlis Zars

Instructors: Caio Avelino

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Get in-depth knowledge of a subject
Intermediate level

Recommended experience

4 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
Get in-depth knowledge of a subject
Intermediate level

Recommended experience

4 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Design and implement comprehensive AI governance frameworks with ethical guidelines, risk assessments, and compliance policies.

  • Build and automate secure MLOps pipelines while conducting systematic audits for bias, fairness, and responsible AI deployment.

  • Optimize AI operations through cloud cost management, security assessments, and performance monitoring across enterprise systems.

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Taught in English
Recently updated!

January 2026

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Specialization - 7 course series

What you'll learn

  • Evaluate AI use cases by applying key Responsible AI principles such as fairness, transparency, and accountability.

  • Identify and document potential risks and biases across data, models, and user interactions using structured ethical design tools.

  • Develop and communicate stakeholder-ready presentations and documentation that clearly articulate Responsible AI design decisions.

Skills you'll gain

Category: Stakeholder Communications
Category: Responsible AI
Category: Ethical Standards And Conduct
Category: Case Studies
Category: Stakeholder Analysis
Category: Accountability
Category: Technical Communication
Category: Design
Category: Presentations
Category: Artificial Intelligence
Category: Data Storytelling
Category: Data Ethics
Category: Governance
Category: Risk Management
Category: Risk Mitigation
Category: Project Documentation

What you'll learn

  • Performance monitoring is essential for maintaining AI system reliability and fairness across diverse user populations

  • Technical architecture decisions (fine-tuning vs RAG) require systematic evaluation of costs, capabilities, and maintenance requirements

  • Effective AI governance requires proactive policy creation, technical guardrails, and cross-functional collaboration to ensure responsible deployment

  • Sustainable AI operations depend on establishing measurable quality benchmarks and continuous feedback loops

Skills you'll gain

Category: Risk Management
Category: Large Language Modeling
Category: Prompt Engineering
Category: Gap Analysis
Category: Data-Driven Decision-Making
Category: Performance Metric
Category: Performance Analysis
Category: AI Security
Category: Governance
Category: Model Evaluation
Category: Responsible AI
Category: System Monitoring
Category: Cost Benefit Analysis
Category: Content Performance Analysis
Category: Governance Risk Management and Compliance
Category: Retrieval-Augmented Generation
Category: Quality Assessment
Category: Cross-Functional Team Leadership
Category: Compliance Management
Category: Generative AI

What you'll learn

  • Learners will be able to systematically analyze data access patterns to recommend role-based controls, evaluate organizational governance maturity ag

Skills you'll gain

Category: Governance
Category: Data Management
Category: Data Governance
Category: Data Quality
Category: Data Security
Category: Quality Assurance and Control
Category: Benchmarking
Category: Role-Based Access Control (RBAC)
Category: Generative AI
Category: Responsible AI
Category: Data Access
Category: Identity and Access Management
Category: AI Security
Category: SQL
Category: Metadata Management

What you'll learn

Skills you'll gain

Category: Technology Roadmaps
Category: Organizational Strategy
Category: Data Ethics
Category: Business Ethics
Category: Governance
Category: Decision Making
Category: Enterprise Architecture
Category: Risk Analysis
Category: Artificial Intelligence and Machine Learning (AI/ML)
Category: Artificial Intelligence
Category: Cross-Functional Collaboration
Category: Responsible AI
Category: Data Governance
Category: Strategic Leadership

What you'll learn

  • Reliable MLOps depends on systematic diagnosis: performance issues are solved by log analysis and pipeline investigation, not guesswork.

  • Governance must be automated into deployment—responsible AI needs CI/CD checks for fairness, explainability, and safe rollbacks, not manual reviews.

  • Adaptive systems need intelligent automation—production models should monitor drift and trigger retraining automatically to stay accurate.

  • Operational excellence requires end-to-end visibility, strong monitoring, versioning and audit trails enable fast debugging and long-term reliability

Skills you'll gain

Category: Continuous Monitoring
Category: Continuous Delivery
Category: Continuous Deployment
Category: Data Pipelines
Category: Model Evaluation
Category: CI/CD
Category: MLOps (Machine Learning Operations)
Category: Data Governance
Category: Model Deployment
Category: Responsible AI
Category: Continuous Integration
Category: Cloud Platforms
Category: Performance Analysis
Category: Automation
Category: Performance Tuning

What you'll learn

  • Create comprehensive documentation and conduct ethical evaluations of large language model systems to ensure responsible AI deployment.

Skills you'll gain

Category: Auditing
Category: Responsible AI
Category: Data Ethics
Category: Technical Documentation
Category: Model Deployment
Category: Mitigation
Category: Data Quality
Category: Accountability
Category: Business Ethics
Category: Compliance Management
Category: Project Documentation
Category: MLOps (Machine Learning Operations)
Category: Compliance Auditing
Category: Ethical Standards And Conduct
Category: Model Evaluation
Category: Case Studies

What you'll learn

  • Map model metrics to business metrics, and define baselines, counterfactuals, and a measurement plan.

  • Design experiments, compute lift and confidence intervals, and plan guardrails.

  • Quantify ROI and risk, build an impact dashboard, and craft an executive story with clear next steps.

Skills you'll gain

Category: Business Metrics
Category: A/B Testing
Category: Dashboard
Category: Return On Investment
Category: Machine Learning
Category: Performance Measurement
Category: Financial Analysis
Category: Business
Category: Model Evaluation
Category: Key Performance Indicators (KPIs)
Category: Power Electronics
Category: Performance Analysis
Category: Experimentation
Category: Stakeholder Communications
Category: Sample Size Determination
Category: Data Storytelling
Category: Product Management
Category: Analysis
Category: Business Valuation

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Instructors

Caio Avelino
7 Courses7,135 learners
Starweaver
Coursera
511 Courses925,886 learners
Karlis Zars
32 Courses53,118 learners

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Coursera

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