COURSE FORMAT & DELIVERY DETAILS Self-Paced. Immediate Access. Lifetime Updates. Zero Risk.
Enroll today and begin your transformation into a certified Scaled Agile leader equipped for the demands of AI-driven enterprise innovation. This course is designed for professionals who need clarity, control, and career acceleration without unnecessary time commitments or learning friction. Designed for Maximum Flexibility and Immediate Impact
The Scaled Agile Framework Mastery for AI-Driven Organizations course is delivered entirely online, allowing you to learn at your own pace from any location, at any time. With no fixed start dates, deadlines, or scheduling constraints, this on-demand program integrates seamlessly into even the busiest professional lives. - You gain instant access to the full course portal upon enrollment, giving you the freedom to begin immediately or return at your convenience
- Most learners complete the program within 4 to 6 weeks when dedicating 6 to 8 hours per week, though many report applying core principles to real projects in as little as 10 days
- The curriculum is structured in bite-sized, high-impact segments so you can progress quickly while retaining deep understanding
- Lifetime access ensures you never lose your learning resources - revisit modules, refresh skills, and grow alongside evolving AI and Agile landscapes
- All course materials are mobile-friendly, fully responsive, and accessible 24/7 across devices including smartphones, tablets, and work laptops
Expert Support When You Need It
Although self-directed, this is not a solitary learning experience. You are supported by dedicated instructor guidance through structured feedback loops, curated practice exercises, and expert-reviewed templates. Our learning ecosystem is built on proven pedagogical design that anticipates your questions before you ask them. Real-World Applicability Built In
Worried this won’t work for your role or industry? Consider this: executives, product managers, AI team leads, Scrum Masters, and engineering directors from finance, healthcare, tech, and government sectors have all successfully applied this material to drive measurable change. - If you're a project manager overwhelmed by AI integration timelines, you'll learn how to align sprints with AI model deployment cycles
- If you're a technology lead struggling with cross-team dependencies, you'll implement synchronized planning techniques that eliminate bottlenecks
- If you're an executive needing faster innovation throughput, you'll master portfolio-level prioritization aligned with AI strategy
This works even if your organization hasn't adopted Agile officially, even if AI initiatives are still in pilot phase, and even if you're operating without formal authority. The tools are designed to create influence through clarity, not hierarchy. Trusted Certification from The Art of Service
Upon successful completion, you will earn a Certificate of Completion issued by The Art of Service - a globally recognized institution with over 250,000 professionals trained in high-impact methodologies across 147 countries. This certification is not just a credential. It is evidence of your ability to lead complex, AI-augmented delivery at scale. Employers consistently recognize The Art of Service credentials for their practical rigor, real-world applicability, and alignment with industry best practices. Your certificate will be digitally verifiable and suitable for LinkedIn, portfolios, and internal advancement reviews. Transparent, One-Time Pricing - No Hidden Fees
The course fee includes everything. There are no additional costs, no subscription traps, and no surprise charges. What you see is what you get: complete access to all materials, ongoing future updates, and your certification - all for a single, straightforward investment. Full Payment Flexibility and Easy Enrollment
We accept all major payment methods including Visa, Mastercard, and PayPal. Your transaction is encrypted with bank-level security, ensuring complete data protection from enrollment to completion. Enroll with Complete Confidence: Our Satisfied or Refunded Promise
Your success is our priority. That’s why we offer a full satisfaction guarantee. If you engage meaningfully with the material and find it does not meet your expectations for quality, relevance, or ROI, contact us for a prompt refund. There is zero financial risk in starting today. After enrollment, you will receive a confirmation email acknowledging your participation. Once the course materials are fully prepared, your access details will be sent in a separate communication, ensuring a smooth and reliable onboarding experience. Turn Uncertainty into Advantage
This course eliminates the trial-and-error approach to scaling Agile in fast-evolving AI environments. You’ll follow a proven, step-by-step methodology used by top-performing teams worldwide. Every decision, template, and technique has been battle-tested in real enterprise settings. You’re not just learning theory. You’re installing an operational advantage that translates directly into faster delivery, higher team alignment, and stronger executive influence. The risk of inaction far outweighs the risk of enrollment - and with our guarantee, there is no downside.
EXTENSIVE & DETAILED COURSE CURRICULUM
Module 1: Foundations of Scaling Agility in the Age of AI - Understanding the convergence of Agile principles and AI operational demands
- Key differences between traditional Agile and AI-driven Agile at scale
- Core challenges in managing AI project uncertainty with iterative delivery
- The role of empirical process control in AI model development cycles
- Why traditional project management fails in AI environments
- Principles of adaptability, inspect-and-adapt, and fast feedback in AI teams
- Defining value in AI-driven organizations beyond feature delivery
- Mapping AI delivery outcomes to business KPIs and strategic goals
- Common failure patterns in AI Agile adoption and how to avoid them
- Establishing psychological safety in high-stakes AI innovation teams
Module 2: Introduction to Scaled Agile Framework (SAFe) Core Concepts - Overview of SAFe’s four configurations and their relevance to AI
- The nine SAFe principles and their adaptation to AI workflows
- Decentralized decision-making in AI model governance
- System thinking applied to AI development pipelines
- Understanding the Agile Release Train (ART) in AI contexts
- Defining PI (Program Increment) objectives for AI sprints
- The role of cadence and synchronization in AI experiment cycles
- Building cross-functional AI and software delivery teams
- Integrating data scientists, ML engineers, and product owners
- Setting team-level iteration goals aligned with model performance targets
Module 3: AI-Driven Value Streams and Solution Trains - Identifying AI-enabled value streams across enterprise operations
- Mapping AI use cases to customer and operational outcomes
- Designing Solution Trains for end-to-end AI product delivery
- Defining AI solution boundaries and system architecture alignment
- Aligning AI initiatives with enterprise architecture roadmaps
- Managing dependencies between data infrastructure and model deployment
- Creating flow efficiency in AI experimentation pipelines
- Establishing feedback loops between model performance and backlog refinement
- Using objective metrics to validate AI value delivery
- Reducing time-to-insight through continuous integration of AI components
Module 4: Team and Technical Agility for AI Practitioners - Building high-performing AI delivery teams using Agile practices
- Role clarity between data engineers, ML Ops, and domain experts
- Implementing Test-Driven Development for AI model validation
- Version control for datasets, models, and pipelines
- Continuous integration and deployment (CI/CD) for machine learning
- Automating retraining cycles within Agile iterations
- Establishing team-level quality gates for AI output
- Pair programming and code review practices for Jupyter notebooks
- Adopting clean code principles in Python and R for reproducibility
- Measuring technical debt in AI systems and mitigation strategies
Module 5: Agile Product Management in AI Environments - Shaping AI product vision with stakeholder alignment
- Developing AI product roadmaps with uncertainty buffers
- Writing AI-ready user stories with measurable acceptance criteria
- Backlog prioritization techniques for AI experimentation
- Managing trade-offs between model accuracy, speed, and ethics
- Integrating customer feedback into AI model refinement
- Defining MVPs for AI features based on learning objectives
- Using outcome-based planning instead of output-based estimation
- Facilitating AI demo sessions with business and technical stakeholders
- Conducting effective iteration reviews for model performance
Module 6: PI Planning for AI Projects - Preparing for PI Planning with AI data and infrastructure readiness
- Setting PI objectives focused on measurable AI outcomes
- Aligning AI sprints with data availability and compute capacity
- Identifying cross-team dependencies in model training workflows
- Managing risks in AI data pipelines during PI planning
- Creating realistic commitments for model accuracy improvements
- Facilitating remote PI planning for distributed AI teams
- Visualizing AI progress with Program Boards and dependency tracking
- Handling mid-PI changes in AI data sources or regulatory requirements
- Using confidence votes to assess AI deliverability
Module 7: Continuous Delivery Pipeline for AI Systems - Designing CI/CD pipelines for machine learning models
- Integrating automated testing for data quality and model drift
- Implementing canary deployments for AI inference services
- Managing A/B testing of multiple model versions in production
- Monitoring model performance with real-time dashboards
- Setting up alerts for data distribution shifts and concept drift
- Automating rollback procedures for failed model updates
- Securing AI pipelines against data poisoning and adversarial attacks
- Versioning models, features, and environments for reproducibility
- Using feature stores to standardize inputs across AI services
Module 8: DevOps and MLOps Integration in SAFe - Extending DevOps principles to machine learning operations
- Creating shared ownership of AI system reliability
- Defining SLAs for AI model responsiveness and uptime
- Integrating compliance and audit requirements into deployment
- Automating model documentation and lineage tracking
- Building feedback loops from production monitoring to backlog
- Scaling infrastructure dynamically based on AI workload
- Implementing infrastructure as code for AI environments
- Managing secrets and access controls in AI systems
- Training operations teams to support AI service incidents
Module 9: Lean Portfolio Management for AI Strategy - Aligning AI initiatives with enterprise strategic themes
- Using Lean business cases for AI investment decisions
- Evaluating AI opportunities with weighted shortest job first (WSJF)
- Creating portfolio backlogs for AI innovation pipelines
- Defining guardrails for responsible AI development
- Establishing funding models for experimental AI programs
- Measuring ROI on AI projects beyond cost savings
- Integrating ethical review into AI portfolio governance
- Managing technical runway for AI infrastructure investments
- Tracking capacity allocation across AI and traditional delivery
Module 10: Leading Change in AI-Oriented Agile Transformations - Identifying change agents within AI and data science teams
- Overcoming resistance to Agile in research-oriented cultures
- Communicating the value of process structure to data scientists
- Building executive sponsorship for AI Agile adoption
- Creating communities of practice for AI practitioners
- Running pilot Agile cycles with cross-functional AI teams
- Scaling successful AI Agile practices across the enterprise
- Using metrics to demonstrate transformation progress
- Managing transformation fatigue in high-velocity AI teams
- Embedding Agile mindset through coaching, not compliance
Module 11: Metrics and KPIs for AI-Driven Agile Performance - Defining Agile metrics that matter in AI environments
- Tracking lead time for data-to-deployment cycles
- Measuring deployment frequency of AI model updates
- Monitoring mean time to recovery (MTTR) for AI service outages
- Using change fail rate to improve AI release quality
- Balancing speed, stability, and security in AI delivery
- Creating dashboards for executive visibility into AI progress
- Linking team metrics to business outcomes and learning velocity
- Avoiding vanity metrics in AI model performance reporting
- Using outcome-based measurement over activity tracking
Module 12: Coaching and Facilitating Agile Ceremonies with AI Teams - Running effective stand-ups with remote AI research teams
- Facilitating backlog refinement for AI experimentation sprints
- Leading iteration planning with probabilistic model outcomes
- Conducting retrospectives that improve AI team dynamics
- Adapting SAFe ceremonies for asynchronous global collaboration
- Using visual facilitation tools for complex AI architecture discussions
- Managing time-boxed events with deep technical teams
- Encouraging participation from introverted data science experts
- Resolving conflicts between model accuracy and delivery speed
- Creating psychological safety in high-pressure AI delivery cycles
Module 13: Architecting for AI at Scale - Designing modular AI systems for continuous delivery
- Defining non-functional requirements for AI services
- Managing technical debt in evolving AI architectures
- Integrating AI models with legacy enterprise systems
- Building scalability into AI inference and training workloads
- Ensuring data privacy and compliance in AI design
- Implementing model interpretability features by design
- Designing for bias detection and mitigation in production
- Aligning AI architecture with security and regulatory standards
- Creating reusable AI components across business units
Module 14: Risk and Compliance Management in AI Delivery - Integrating governance into Agile AI workflows
- Managing regulatory compliance in fast-moving AI sprints
- Documenting model decisions for audit and review
- Conducting ethical impact assessments for AI features
- Handling data privacy requirements in model development
- Implementing model cards and datasheets for transparency
- Managing third-party AI vendor risks and dependencies
- Creating incident response plans for AI failures
- Aligning AI projects with corporate risk appetite
- Building compliance into automated testing and deployment
Module 15: Real-World Implementation: Case Studies and Simulations - Case study: Financial fraud detection system using SAFe
- Case study: Healthcare diagnostics AI rollout at scale
- Case study: AI-powered customer service transformation
- Simulated PI Planning for an autonomous vehicle AI team
- End-to-end Agile delivery simulation for a recommendation engine
- Responding to data source failure during a sprint
- Managing unexpected model bias discovery in production
- Handling regulatory changes mid-PI
- Re-scoping AI objectives due to infrastructure delays
- Aligning executive strategy with team-level AI delivery
Module 16: Certification Preparation and Career Advancement - Reviewing key SAFe concepts for mastery assessment
- Practicing scenario-based questions for real-world application
- Mapping your experience to certification competencies
- Preparing your personal Agile leadership narrative
- Adding the Certificate of Completion to your professional profile
- Leveraging your credential in performance reviews and promotions
- Optimizing LinkedIn and resume language for Agile AI roles
- Networking with other certified professionals globally
- Accessing continued learning pathways from The Art of Service
- Planning your next career move with certified credibility
Module 1: Foundations of Scaling Agility in the Age of AI - Understanding the convergence of Agile principles and AI operational demands
- Key differences between traditional Agile and AI-driven Agile at scale
- Core challenges in managing AI project uncertainty with iterative delivery
- The role of empirical process control in AI model development cycles
- Why traditional project management fails in AI environments
- Principles of adaptability, inspect-and-adapt, and fast feedback in AI teams
- Defining value in AI-driven organizations beyond feature delivery
- Mapping AI delivery outcomes to business KPIs and strategic goals
- Common failure patterns in AI Agile adoption and how to avoid them
- Establishing psychological safety in high-stakes AI innovation teams
Module 2: Introduction to Scaled Agile Framework (SAFe) Core Concepts - Overview of SAFe’s four configurations and their relevance to AI
- The nine SAFe principles and their adaptation to AI workflows
- Decentralized decision-making in AI model governance
- System thinking applied to AI development pipelines
- Understanding the Agile Release Train (ART) in AI contexts
- Defining PI (Program Increment) objectives for AI sprints
- The role of cadence and synchronization in AI experiment cycles
- Building cross-functional AI and software delivery teams
- Integrating data scientists, ML engineers, and product owners
- Setting team-level iteration goals aligned with model performance targets
Module 3: AI-Driven Value Streams and Solution Trains - Identifying AI-enabled value streams across enterprise operations
- Mapping AI use cases to customer and operational outcomes
- Designing Solution Trains for end-to-end AI product delivery
- Defining AI solution boundaries and system architecture alignment
- Aligning AI initiatives with enterprise architecture roadmaps
- Managing dependencies between data infrastructure and model deployment
- Creating flow efficiency in AI experimentation pipelines
- Establishing feedback loops between model performance and backlog refinement
- Using objective metrics to validate AI value delivery
- Reducing time-to-insight through continuous integration of AI components
Module 4: Team and Technical Agility for AI Practitioners - Building high-performing AI delivery teams using Agile practices
- Role clarity between data engineers, ML Ops, and domain experts
- Implementing Test-Driven Development for AI model validation
- Version control for datasets, models, and pipelines
- Continuous integration and deployment (CI/CD) for machine learning
- Automating retraining cycles within Agile iterations
- Establishing team-level quality gates for AI output
- Pair programming and code review practices for Jupyter notebooks
- Adopting clean code principles in Python and R for reproducibility
- Measuring technical debt in AI systems and mitigation strategies
Module 5: Agile Product Management in AI Environments - Shaping AI product vision with stakeholder alignment
- Developing AI product roadmaps with uncertainty buffers
- Writing AI-ready user stories with measurable acceptance criteria
- Backlog prioritization techniques for AI experimentation
- Managing trade-offs between model accuracy, speed, and ethics
- Integrating customer feedback into AI model refinement
- Defining MVPs for AI features based on learning objectives
- Using outcome-based planning instead of output-based estimation
- Facilitating AI demo sessions with business and technical stakeholders
- Conducting effective iteration reviews for model performance
Module 6: PI Planning for AI Projects - Preparing for PI Planning with AI data and infrastructure readiness
- Setting PI objectives focused on measurable AI outcomes
- Aligning AI sprints with data availability and compute capacity
- Identifying cross-team dependencies in model training workflows
- Managing risks in AI data pipelines during PI planning
- Creating realistic commitments for model accuracy improvements
- Facilitating remote PI planning for distributed AI teams
- Visualizing AI progress with Program Boards and dependency tracking
- Handling mid-PI changes in AI data sources or regulatory requirements
- Using confidence votes to assess AI deliverability
Module 7: Continuous Delivery Pipeline for AI Systems - Designing CI/CD pipelines for machine learning models
- Integrating automated testing for data quality and model drift
- Implementing canary deployments for AI inference services
- Managing A/B testing of multiple model versions in production
- Monitoring model performance with real-time dashboards
- Setting up alerts for data distribution shifts and concept drift
- Automating rollback procedures for failed model updates
- Securing AI pipelines against data poisoning and adversarial attacks
- Versioning models, features, and environments for reproducibility
- Using feature stores to standardize inputs across AI services
Module 8: DevOps and MLOps Integration in SAFe - Extending DevOps principles to machine learning operations
- Creating shared ownership of AI system reliability
- Defining SLAs for AI model responsiveness and uptime
- Integrating compliance and audit requirements into deployment
- Automating model documentation and lineage tracking
- Building feedback loops from production monitoring to backlog
- Scaling infrastructure dynamically based on AI workload
- Implementing infrastructure as code for AI environments
- Managing secrets and access controls in AI systems
- Training operations teams to support AI service incidents
Module 9: Lean Portfolio Management for AI Strategy - Aligning AI initiatives with enterprise strategic themes
- Using Lean business cases for AI investment decisions
- Evaluating AI opportunities with weighted shortest job first (WSJF)
- Creating portfolio backlogs for AI innovation pipelines
- Defining guardrails for responsible AI development
- Establishing funding models for experimental AI programs
- Measuring ROI on AI projects beyond cost savings
- Integrating ethical review into AI portfolio governance
- Managing technical runway for AI infrastructure investments
- Tracking capacity allocation across AI and traditional delivery
Module 10: Leading Change in AI-Oriented Agile Transformations - Identifying change agents within AI and data science teams
- Overcoming resistance to Agile in research-oriented cultures
- Communicating the value of process structure to data scientists
- Building executive sponsorship for AI Agile adoption
- Creating communities of practice for AI practitioners
- Running pilot Agile cycles with cross-functional AI teams
- Scaling successful AI Agile practices across the enterprise
- Using metrics to demonstrate transformation progress
- Managing transformation fatigue in high-velocity AI teams
- Embedding Agile mindset through coaching, not compliance
Module 11: Metrics and KPIs for AI-Driven Agile Performance - Defining Agile metrics that matter in AI environments
- Tracking lead time for data-to-deployment cycles
- Measuring deployment frequency of AI model updates
- Monitoring mean time to recovery (MTTR) for AI service outages
- Using change fail rate to improve AI release quality
- Balancing speed, stability, and security in AI delivery
- Creating dashboards for executive visibility into AI progress
- Linking team metrics to business outcomes and learning velocity
- Avoiding vanity metrics in AI model performance reporting
- Using outcome-based measurement over activity tracking
Module 12: Coaching and Facilitating Agile Ceremonies with AI Teams - Running effective stand-ups with remote AI research teams
- Facilitating backlog refinement for AI experimentation sprints
- Leading iteration planning with probabilistic model outcomes
- Conducting retrospectives that improve AI team dynamics
- Adapting SAFe ceremonies for asynchronous global collaboration
- Using visual facilitation tools for complex AI architecture discussions
- Managing time-boxed events with deep technical teams
- Encouraging participation from introverted data science experts
- Resolving conflicts between model accuracy and delivery speed
- Creating psychological safety in high-pressure AI delivery cycles
Module 13: Architecting for AI at Scale - Designing modular AI systems for continuous delivery
- Defining non-functional requirements for AI services
- Managing technical debt in evolving AI architectures
- Integrating AI models with legacy enterprise systems
- Building scalability into AI inference and training workloads
- Ensuring data privacy and compliance in AI design
- Implementing model interpretability features by design
- Designing for bias detection and mitigation in production
- Aligning AI architecture with security and regulatory standards
- Creating reusable AI components across business units
Module 14: Risk and Compliance Management in AI Delivery - Integrating governance into Agile AI workflows
- Managing regulatory compliance in fast-moving AI sprints
- Documenting model decisions for audit and review
- Conducting ethical impact assessments for AI features
- Handling data privacy requirements in model development
- Implementing model cards and datasheets for transparency
- Managing third-party AI vendor risks and dependencies
- Creating incident response plans for AI failures
- Aligning AI projects with corporate risk appetite
- Building compliance into automated testing and deployment
Module 15: Real-World Implementation: Case Studies and Simulations - Case study: Financial fraud detection system using SAFe
- Case study: Healthcare diagnostics AI rollout at scale
- Case study: AI-powered customer service transformation
- Simulated PI Planning for an autonomous vehicle AI team
- End-to-end Agile delivery simulation for a recommendation engine
- Responding to data source failure during a sprint
- Managing unexpected model bias discovery in production
- Handling regulatory changes mid-PI
- Re-scoping AI objectives due to infrastructure delays
- Aligning executive strategy with team-level AI delivery
Module 16: Certification Preparation and Career Advancement - Reviewing key SAFe concepts for mastery assessment
- Practicing scenario-based questions for real-world application
- Mapping your experience to certification competencies
- Preparing your personal Agile leadership narrative
- Adding the Certificate of Completion to your professional profile
- Leveraging your credential in performance reviews and promotions
- Optimizing LinkedIn and resume language for Agile AI roles
- Networking with other certified professionals globally
- Accessing continued learning pathways from The Art of Service
- Planning your next career move with certified credibility
- Overview of SAFe’s four configurations and their relevance to AI
- The nine SAFe principles and their adaptation to AI workflows
- Decentralized decision-making in AI model governance
- System thinking applied to AI development pipelines
- Understanding the Agile Release Train (ART) in AI contexts
- Defining PI (Program Increment) objectives for AI sprints
- The role of cadence and synchronization in AI experiment cycles
- Building cross-functional AI and software delivery teams
- Integrating data scientists, ML engineers, and product owners
- Setting team-level iteration goals aligned with model performance targets
Module 3: AI-Driven Value Streams and Solution Trains - Identifying AI-enabled value streams across enterprise operations
- Mapping AI use cases to customer and operational outcomes
- Designing Solution Trains for end-to-end AI product delivery
- Defining AI solution boundaries and system architecture alignment
- Aligning AI initiatives with enterprise architecture roadmaps
- Managing dependencies between data infrastructure and model deployment
- Creating flow efficiency in AI experimentation pipelines
- Establishing feedback loops between model performance and backlog refinement
- Using objective metrics to validate AI value delivery
- Reducing time-to-insight through continuous integration of AI components
Module 4: Team and Technical Agility for AI Practitioners - Building high-performing AI delivery teams using Agile practices
- Role clarity between data engineers, ML Ops, and domain experts
- Implementing Test-Driven Development for AI model validation
- Version control for datasets, models, and pipelines
- Continuous integration and deployment (CI/CD) for machine learning
- Automating retraining cycles within Agile iterations
- Establishing team-level quality gates for AI output
- Pair programming and code review practices for Jupyter notebooks
- Adopting clean code principles in Python and R for reproducibility
- Measuring technical debt in AI systems and mitigation strategies
Module 5: Agile Product Management in AI Environments - Shaping AI product vision with stakeholder alignment
- Developing AI product roadmaps with uncertainty buffers
- Writing AI-ready user stories with measurable acceptance criteria
- Backlog prioritization techniques for AI experimentation
- Managing trade-offs between model accuracy, speed, and ethics
- Integrating customer feedback into AI model refinement
- Defining MVPs for AI features based on learning objectives
- Using outcome-based planning instead of output-based estimation
- Facilitating AI demo sessions with business and technical stakeholders
- Conducting effective iteration reviews for model performance
Module 6: PI Planning for AI Projects - Preparing for PI Planning with AI data and infrastructure readiness
- Setting PI objectives focused on measurable AI outcomes
- Aligning AI sprints with data availability and compute capacity
- Identifying cross-team dependencies in model training workflows
- Managing risks in AI data pipelines during PI planning
- Creating realistic commitments for model accuracy improvements
- Facilitating remote PI planning for distributed AI teams
- Visualizing AI progress with Program Boards and dependency tracking
- Handling mid-PI changes in AI data sources or regulatory requirements
- Using confidence votes to assess AI deliverability
Module 7: Continuous Delivery Pipeline for AI Systems - Designing CI/CD pipelines for machine learning models
- Integrating automated testing for data quality and model drift
- Implementing canary deployments for AI inference services
- Managing A/B testing of multiple model versions in production
- Monitoring model performance with real-time dashboards
- Setting up alerts for data distribution shifts and concept drift
- Automating rollback procedures for failed model updates
- Securing AI pipelines against data poisoning and adversarial attacks
- Versioning models, features, and environments for reproducibility
- Using feature stores to standardize inputs across AI services
Module 8: DevOps and MLOps Integration in SAFe - Extending DevOps principles to machine learning operations
- Creating shared ownership of AI system reliability
- Defining SLAs for AI model responsiveness and uptime
- Integrating compliance and audit requirements into deployment
- Automating model documentation and lineage tracking
- Building feedback loops from production monitoring to backlog
- Scaling infrastructure dynamically based on AI workload
- Implementing infrastructure as code for AI environments
- Managing secrets and access controls in AI systems
- Training operations teams to support AI service incidents
Module 9: Lean Portfolio Management for AI Strategy - Aligning AI initiatives with enterprise strategic themes
- Using Lean business cases for AI investment decisions
- Evaluating AI opportunities with weighted shortest job first (WSJF)
- Creating portfolio backlogs for AI innovation pipelines
- Defining guardrails for responsible AI development
- Establishing funding models for experimental AI programs
- Measuring ROI on AI projects beyond cost savings
- Integrating ethical review into AI portfolio governance
- Managing technical runway for AI infrastructure investments
- Tracking capacity allocation across AI and traditional delivery
Module 10: Leading Change in AI-Oriented Agile Transformations - Identifying change agents within AI and data science teams
- Overcoming resistance to Agile in research-oriented cultures
- Communicating the value of process structure to data scientists
- Building executive sponsorship for AI Agile adoption
- Creating communities of practice for AI practitioners
- Running pilot Agile cycles with cross-functional AI teams
- Scaling successful AI Agile practices across the enterprise
- Using metrics to demonstrate transformation progress
- Managing transformation fatigue in high-velocity AI teams
- Embedding Agile mindset through coaching, not compliance
Module 11: Metrics and KPIs for AI-Driven Agile Performance - Defining Agile metrics that matter in AI environments
- Tracking lead time for data-to-deployment cycles
- Measuring deployment frequency of AI model updates
- Monitoring mean time to recovery (MTTR) for AI service outages
- Using change fail rate to improve AI release quality
- Balancing speed, stability, and security in AI delivery
- Creating dashboards for executive visibility into AI progress
- Linking team metrics to business outcomes and learning velocity
- Avoiding vanity metrics in AI model performance reporting
- Using outcome-based measurement over activity tracking
Module 12: Coaching and Facilitating Agile Ceremonies with AI Teams - Running effective stand-ups with remote AI research teams
- Facilitating backlog refinement for AI experimentation sprints
- Leading iteration planning with probabilistic model outcomes
- Conducting retrospectives that improve AI team dynamics
- Adapting SAFe ceremonies for asynchronous global collaboration
- Using visual facilitation tools for complex AI architecture discussions
- Managing time-boxed events with deep technical teams
- Encouraging participation from introverted data science experts
- Resolving conflicts between model accuracy and delivery speed
- Creating psychological safety in high-pressure AI delivery cycles
Module 13: Architecting for AI at Scale - Designing modular AI systems for continuous delivery
- Defining non-functional requirements for AI services
- Managing technical debt in evolving AI architectures
- Integrating AI models with legacy enterprise systems
- Building scalability into AI inference and training workloads
- Ensuring data privacy and compliance in AI design
- Implementing model interpretability features by design
- Designing for bias detection and mitigation in production
- Aligning AI architecture with security and regulatory standards
- Creating reusable AI components across business units
Module 14: Risk and Compliance Management in AI Delivery - Integrating governance into Agile AI workflows
- Managing regulatory compliance in fast-moving AI sprints
- Documenting model decisions for audit and review
- Conducting ethical impact assessments for AI features
- Handling data privacy requirements in model development
- Implementing model cards and datasheets for transparency
- Managing third-party AI vendor risks and dependencies
- Creating incident response plans for AI failures
- Aligning AI projects with corporate risk appetite
- Building compliance into automated testing and deployment
Module 15: Real-World Implementation: Case Studies and Simulations - Case study: Financial fraud detection system using SAFe
- Case study: Healthcare diagnostics AI rollout at scale
- Case study: AI-powered customer service transformation
- Simulated PI Planning for an autonomous vehicle AI team
- End-to-end Agile delivery simulation for a recommendation engine
- Responding to data source failure during a sprint
- Managing unexpected model bias discovery in production
- Handling regulatory changes mid-PI
- Re-scoping AI objectives due to infrastructure delays
- Aligning executive strategy with team-level AI delivery
Module 16: Certification Preparation and Career Advancement - Reviewing key SAFe concepts for mastery assessment
- Practicing scenario-based questions for real-world application
- Mapping your experience to certification competencies
- Preparing your personal Agile leadership narrative
- Adding the Certificate of Completion to your professional profile
- Leveraging your credential in performance reviews and promotions
- Optimizing LinkedIn and resume language for Agile AI roles
- Networking with other certified professionals globally
- Accessing continued learning pathways from The Art of Service
- Planning your next career move with certified credibility
- Building high-performing AI delivery teams using Agile practices
- Role clarity between data engineers, ML Ops, and domain experts
- Implementing Test-Driven Development for AI model validation
- Version control for datasets, models, and pipelines
- Continuous integration and deployment (CI/CD) for machine learning
- Automating retraining cycles within Agile iterations
- Establishing team-level quality gates for AI output
- Pair programming and code review practices for Jupyter notebooks
- Adopting clean code principles in Python and R for reproducibility
- Measuring technical debt in AI systems and mitigation strategies
Module 5: Agile Product Management in AI Environments - Shaping AI product vision with stakeholder alignment
- Developing AI product roadmaps with uncertainty buffers
- Writing AI-ready user stories with measurable acceptance criteria
- Backlog prioritization techniques for AI experimentation
- Managing trade-offs between model accuracy, speed, and ethics
- Integrating customer feedback into AI model refinement
- Defining MVPs for AI features based on learning objectives
- Using outcome-based planning instead of output-based estimation
- Facilitating AI demo sessions with business and technical stakeholders
- Conducting effective iteration reviews for model performance
Module 6: PI Planning for AI Projects - Preparing for PI Planning with AI data and infrastructure readiness
- Setting PI objectives focused on measurable AI outcomes
- Aligning AI sprints with data availability and compute capacity
- Identifying cross-team dependencies in model training workflows
- Managing risks in AI data pipelines during PI planning
- Creating realistic commitments for model accuracy improvements
- Facilitating remote PI planning for distributed AI teams
- Visualizing AI progress with Program Boards and dependency tracking
- Handling mid-PI changes in AI data sources or regulatory requirements
- Using confidence votes to assess AI deliverability
Module 7: Continuous Delivery Pipeline for AI Systems - Designing CI/CD pipelines for machine learning models
- Integrating automated testing for data quality and model drift
- Implementing canary deployments for AI inference services
- Managing A/B testing of multiple model versions in production
- Monitoring model performance with real-time dashboards
- Setting up alerts for data distribution shifts and concept drift
- Automating rollback procedures for failed model updates
- Securing AI pipelines against data poisoning and adversarial attacks
- Versioning models, features, and environments for reproducibility
- Using feature stores to standardize inputs across AI services
Module 8: DevOps and MLOps Integration in SAFe - Extending DevOps principles to machine learning operations
- Creating shared ownership of AI system reliability
- Defining SLAs for AI model responsiveness and uptime
- Integrating compliance and audit requirements into deployment
- Automating model documentation and lineage tracking
- Building feedback loops from production monitoring to backlog
- Scaling infrastructure dynamically based on AI workload
- Implementing infrastructure as code for AI environments
- Managing secrets and access controls in AI systems
- Training operations teams to support AI service incidents
Module 9: Lean Portfolio Management for AI Strategy - Aligning AI initiatives with enterprise strategic themes
- Using Lean business cases for AI investment decisions
- Evaluating AI opportunities with weighted shortest job first (WSJF)
- Creating portfolio backlogs for AI innovation pipelines
- Defining guardrails for responsible AI development
- Establishing funding models for experimental AI programs
- Measuring ROI on AI projects beyond cost savings
- Integrating ethical review into AI portfolio governance
- Managing technical runway for AI infrastructure investments
- Tracking capacity allocation across AI and traditional delivery
Module 10: Leading Change in AI-Oriented Agile Transformations - Identifying change agents within AI and data science teams
- Overcoming resistance to Agile in research-oriented cultures
- Communicating the value of process structure to data scientists
- Building executive sponsorship for AI Agile adoption
- Creating communities of practice for AI practitioners
- Running pilot Agile cycles with cross-functional AI teams
- Scaling successful AI Agile practices across the enterprise
- Using metrics to demonstrate transformation progress
- Managing transformation fatigue in high-velocity AI teams
- Embedding Agile mindset through coaching, not compliance
Module 11: Metrics and KPIs for AI-Driven Agile Performance - Defining Agile metrics that matter in AI environments
- Tracking lead time for data-to-deployment cycles
- Measuring deployment frequency of AI model updates
- Monitoring mean time to recovery (MTTR) for AI service outages
- Using change fail rate to improve AI release quality
- Balancing speed, stability, and security in AI delivery
- Creating dashboards for executive visibility into AI progress
- Linking team metrics to business outcomes and learning velocity
- Avoiding vanity metrics in AI model performance reporting
- Using outcome-based measurement over activity tracking
Module 12: Coaching and Facilitating Agile Ceremonies with AI Teams - Running effective stand-ups with remote AI research teams
- Facilitating backlog refinement for AI experimentation sprints
- Leading iteration planning with probabilistic model outcomes
- Conducting retrospectives that improve AI team dynamics
- Adapting SAFe ceremonies for asynchronous global collaboration
- Using visual facilitation tools for complex AI architecture discussions
- Managing time-boxed events with deep technical teams
- Encouraging participation from introverted data science experts
- Resolving conflicts between model accuracy and delivery speed
- Creating psychological safety in high-pressure AI delivery cycles
Module 13: Architecting for AI at Scale - Designing modular AI systems for continuous delivery
- Defining non-functional requirements for AI services
- Managing technical debt in evolving AI architectures
- Integrating AI models with legacy enterprise systems
- Building scalability into AI inference and training workloads
- Ensuring data privacy and compliance in AI design
- Implementing model interpretability features by design
- Designing for bias detection and mitigation in production
- Aligning AI architecture with security and regulatory standards
- Creating reusable AI components across business units
Module 14: Risk and Compliance Management in AI Delivery - Integrating governance into Agile AI workflows
- Managing regulatory compliance in fast-moving AI sprints
- Documenting model decisions for audit and review
- Conducting ethical impact assessments for AI features
- Handling data privacy requirements in model development
- Implementing model cards and datasheets for transparency
- Managing third-party AI vendor risks and dependencies
- Creating incident response plans for AI failures
- Aligning AI projects with corporate risk appetite
- Building compliance into automated testing and deployment
Module 15: Real-World Implementation: Case Studies and Simulations - Case study: Financial fraud detection system using SAFe
- Case study: Healthcare diagnostics AI rollout at scale
- Case study: AI-powered customer service transformation
- Simulated PI Planning for an autonomous vehicle AI team
- End-to-end Agile delivery simulation for a recommendation engine
- Responding to data source failure during a sprint
- Managing unexpected model bias discovery in production
- Handling regulatory changes mid-PI
- Re-scoping AI objectives due to infrastructure delays
- Aligning executive strategy with team-level AI delivery
Module 16: Certification Preparation and Career Advancement - Reviewing key SAFe concepts for mastery assessment
- Practicing scenario-based questions for real-world application
- Mapping your experience to certification competencies
- Preparing your personal Agile leadership narrative
- Adding the Certificate of Completion to your professional profile
- Leveraging your credential in performance reviews and promotions
- Optimizing LinkedIn and resume language for Agile AI roles
- Networking with other certified professionals globally
- Accessing continued learning pathways from The Art of Service
- Planning your next career move with certified credibility
- Preparing for PI Planning with AI data and infrastructure readiness
- Setting PI objectives focused on measurable AI outcomes
- Aligning AI sprints with data availability and compute capacity
- Identifying cross-team dependencies in model training workflows
- Managing risks in AI data pipelines during PI planning
- Creating realistic commitments for model accuracy improvements
- Facilitating remote PI planning for distributed AI teams
- Visualizing AI progress with Program Boards and dependency tracking
- Handling mid-PI changes in AI data sources or regulatory requirements
- Using confidence votes to assess AI deliverability
Module 7: Continuous Delivery Pipeline for AI Systems - Designing CI/CD pipelines for machine learning models
- Integrating automated testing for data quality and model drift
- Implementing canary deployments for AI inference services
- Managing A/B testing of multiple model versions in production
- Monitoring model performance with real-time dashboards
- Setting up alerts for data distribution shifts and concept drift
- Automating rollback procedures for failed model updates
- Securing AI pipelines against data poisoning and adversarial attacks
- Versioning models, features, and environments for reproducibility
- Using feature stores to standardize inputs across AI services
Module 8: DevOps and MLOps Integration in SAFe - Extending DevOps principles to machine learning operations
- Creating shared ownership of AI system reliability
- Defining SLAs for AI model responsiveness and uptime
- Integrating compliance and audit requirements into deployment
- Automating model documentation and lineage tracking
- Building feedback loops from production monitoring to backlog
- Scaling infrastructure dynamically based on AI workload
- Implementing infrastructure as code for AI environments
- Managing secrets and access controls in AI systems
- Training operations teams to support AI service incidents
Module 9: Lean Portfolio Management for AI Strategy - Aligning AI initiatives with enterprise strategic themes
- Using Lean business cases for AI investment decisions
- Evaluating AI opportunities with weighted shortest job first (WSJF)
- Creating portfolio backlogs for AI innovation pipelines
- Defining guardrails for responsible AI development
- Establishing funding models for experimental AI programs
- Measuring ROI on AI projects beyond cost savings
- Integrating ethical review into AI portfolio governance
- Managing technical runway for AI infrastructure investments
- Tracking capacity allocation across AI and traditional delivery
Module 10: Leading Change in AI-Oriented Agile Transformations - Identifying change agents within AI and data science teams
- Overcoming resistance to Agile in research-oriented cultures
- Communicating the value of process structure to data scientists
- Building executive sponsorship for AI Agile adoption
- Creating communities of practice for AI practitioners
- Running pilot Agile cycles with cross-functional AI teams
- Scaling successful AI Agile practices across the enterprise
- Using metrics to demonstrate transformation progress
- Managing transformation fatigue in high-velocity AI teams
- Embedding Agile mindset through coaching, not compliance
Module 11: Metrics and KPIs for AI-Driven Agile Performance - Defining Agile metrics that matter in AI environments
- Tracking lead time for data-to-deployment cycles
- Measuring deployment frequency of AI model updates
- Monitoring mean time to recovery (MTTR) for AI service outages
- Using change fail rate to improve AI release quality
- Balancing speed, stability, and security in AI delivery
- Creating dashboards for executive visibility into AI progress
- Linking team metrics to business outcomes and learning velocity
- Avoiding vanity metrics in AI model performance reporting
- Using outcome-based measurement over activity tracking
Module 12: Coaching and Facilitating Agile Ceremonies with AI Teams - Running effective stand-ups with remote AI research teams
- Facilitating backlog refinement for AI experimentation sprints
- Leading iteration planning with probabilistic model outcomes
- Conducting retrospectives that improve AI team dynamics
- Adapting SAFe ceremonies for asynchronous global collaboration
- Using visual facilitation tools for complex AI architecture discussions
- Managing time-boxed events with deep technical teams
- Encouraging participation from introverted data science experts
- Resolving conflicts between model accuracy and delivery speed
- Creating psychological safety in high-pressure AI delivery cycles
Module 13: Architecting for AI at Scale - Designing modular AI systems for continuous delivery
- Defining non-functional requirements for AI services
- Managing technical debt in evolving AI architectures
- Integrating AI models with legacy enterprise systems
- Building scalability into AI inference and training workloads
- Ensuring data privacy and compliance in AI design
- Implementing model interpretability features by design
- Designing for bias detection and mitigation in production
- Aligning AI architecture with security and regulatory standards
- Creating reusable AI components across business units
Module 14: Risk and Compliance Management in AI Delivery - Integrating governance into Agile AI workflows
- Managing regulatory compliance in fast-moving AI sprints
- Documenting model decisions for audit and review
- Conducting ethical impact assessments for AI features
- Handling data privacy requirements in model development
- Implementing model cards and datasheets for transparency
- Managing third-party AI vendor risks and dependencies
- Creating incident response plans for AI failures
- Aligning AI projects with corporate risk appetite
- Building compliance into automated testing and deployment
Module 15: Real-World Implementation: Case Studies and Simulations - Case study: Financial fraud detection system using SAFe
- Case study: Healthcare diagnostics AI rollout at scale
- Case study: AI-powered customer service transformation
- Simulated PI Planning for an autonomous vehicle AI team
- End-to-end Agile delivery simulation for a recommendation engine
- Responding to data source failure during a sprint
- Managing unexpected model bias discovery in production
- Handling regulatory changes mid-PI
- Re-scoping AI objectives due to infrastructure delays
- Aligning executive strategy with team-level AI delivery
Module 16: Certification Preparation and Career Advancement - Reviewing key SAFe concepts for mastery assessment
- Practicing scenario-based questions for real-world application
- Mapping your experience to certification competencies
- Preparing your personal Agile leadership narrative
- Adding the Certificate of Completion to your professional profile
- Leveraging your credential in performance reviews and promotions
- Optimizing LinkedIn and resume language for Agile AI roles
- Networking with other certified professionals globally
- Accessing continued learning pathways from The Art of Service
- Planning your next career move with certified credibility
- Extending DevOps principles to machine learning operations
- Creating shared ownership of AI system reliability
- Defining SLAs for AI model responsiveness and uptime
- Integrating compliance and audit requirements into deployment
- Automating model documentation and lineage tracking
- Building feedback loops from production monitoring to backlog
- Scaling infrastructure dynamically based on AI workload
- Implementing infrastructure as code for AI environments
- Managing secrets and access controls in AI systems
- Training operations teams to support AI service incidents
Module 9: Lean Portfolio Management for AI Strategy - Aligning AI initiatives with enterprise strategic themes
- Using Lean business cases for AI investment decisions
- Evaluating AI opportunities with weighted shortest job first (WSJF)
- Creating portfolio backlogs for AI innovation pipelines
- Defining guardrails for responsible AI development
- Establishing funding models for experimental AI programs
- Measuring ROI on AI projects beyond cost savings
- Integrating ethical review into AI portfolio governance
- Managing technical runway for AI infrastructure investments
- Tracking capacity allocation across AI and traditional delivery
Module 10: Leading Change in AI-Oriented Agile Transformations - Identifying change agents within AI and data science teams
- Overcoming resistance to Agile in research-oriented cultures
- Communicating the value of process structure to data scientists
- Building executive sponsorship for AI Agile adoption
- Creating communities of practice for AI practitioners
- Running pilot Agile cycles with cross-functional AI teams
- Scaling successful AI Agile practices across the enterprise
- Using metrics to demonstrate transformation progress
- Managing transformation fatigue in high-velocity AI teams
- Embedding Agile mindset through coaching, not compliance
Module 11: Metrics and KPIs for AI-Driven Agile Performance - Defining Agile metrics that matter in AI environments
- Tracking lead time for data-to-deployment cycles
- Measuring deployment frequency of AI model updates
- Monitoring mean time to recovery (MTTR) for AI service outages
- Using change fail rate to improve AI release quality
- Balancing speed, stability, and security in AI delivery
- Creating dashboards for executive visibility into AI progress
- Linking team metrics to business outcomes and learning velocity
- Avoiding vanity metrics in AI model performance reporting
- Using outcome-based measurement over activity tracking
Module 12: Coaching and Facilitating Agile Ceremonies with AI Teams - Running effective stand-ups with remote AI research teams
- Facilitating backlog refinement for AI experimentation sprints
- Leading iteration planning with probabilistic model outcomes
- Conducting retrospectives that improve AI team dynamics
- Adapting SAFe ceremonies for asynchronous global collaboration
- Using visual facilitation tools for complex AI architecture discussions
- Managing time-boxed events with deep technical teams
- Encouraging participation from introverted data science experts
- Resolving conflicts between model accuracy and delivery speed
- Creating psychological safety in high-pressure AI delivery cycles
Module 13: Architecting for AI at Scale - Designing modular AI systems for continuous delivery
- Defining non-functional requirements for AI services
- Managing technical debt in evolving AI architectures
- Integrating AI models with legacy enterprise systems
- Building scalability into AI inference and training workloads
- Ensuring data privacy and compliance in AI design
- Implementing model interpretability features by design
- Designing for bias detection and mitigation in production
- Aligning AI architecture with security and regulatory standards
- Creating reusable AI components across business units
Module 14: Risk and Compliance Management in AI Delivery - Integrating governance into Agile AI workflows
- Managing regulatory compliance in fast-moving AI sprints
- Documenting model decisions for audit and review
- Conducting ethical impact assessments for AI features
- Handling data privacy requirements in model development
- Implementing model cards and datasheets for transparency
- Managing third-party AI vendor risks and dependencies
- Creating incident response plans for AI failures
- Aligning AI projects with corporate risk appetite
- Building compliance into automated testing and deployment
Module 15: Real-World Implementation: Case Studies and Simulations - Case study: Financial fraud detection system using SAFe
- Case study: Healthcare diagnostics AI rollout at scale
- Case study: AI-powered customer service transformation
- Simulated PI Planning for an autonomous vehicle AI team
- End-to-end Agile delivery simulation for a recommendation engine
- Responding to data source failure during a sprint
- Managing unexpected model bias discovery in production
- Handling regulatory changes mid-PI
- Re-scoping AI objectives due to infrastructure delays
- Aligning executive strategy with team-level AI delivery
Module 16: Certification Preparation and Career Advancement - Reviewing key SAFe concepts for mastery assessment
- Practicing scenario-based questions for real-world application
- Mapping your experience to certification competencies
- Preparing your personal Agile leadership narrative
- Adding the Certificate of Completion to your professional profile
- Leveraging your credential in performance reviews and promotions
- Optimizing LinkedIn and resume language for Agile AI roles
- Networking with other certified professionals globally
- Accessing continued learning pathways from The Art of Service
- Planning your next career move with certified credibility
- Identifying change agents within AI and data science teams
- Overcoming resistance to Agile in research-oriented cultures
- Communicating the value of process structure to data scientists
- Building executive sponsorship for AI Agile adoption
- Creating communities of practice for AI practitioners
- Running pilot Agile cycles with cross-functional AI teams
- Scaling successful AI Agile practices across the enterprise
- Using metrics to demonstrate transformation progress
- Managing transformation fatigue in high-velocity AI teams
- Embedding Agile mindset through coaching, not compliance
Module 11: Metrics and KPIs for AI-Driven Agile Performance - Defining Agile metrics that matter in AI environments
- Tracking lead time for data-to-deployment cycles
- Measuring deployment frequency of AI model updates
- Monitoring mean time to recovery (MTTR) for AI service outages
- Using change fail rate to improve AI release quality
- Balancing speed, stability, and security in AI delivery
- Creating dashboards for executive visibility into AI progress
- Linking team metrics to business outcomes and learning velocity
- Avoiding vanity metrics in AI model performance reporting
- Using outcome-based measurement over activity tracking
Module 12: Coaching and Facilitating Agile Ceremonies with AI Teams - Running effective stand-ups with remote AI research teams
- Facilitating backlog refinement for AI experimentation sprints
- Leading iteration planning with probabilistic model outcomes
- Conducting retrospectives that improve AI team dynamics
- Adapting SAFe ceremonies for asynchronous global collaboration
- Using visual facilitation tools for complex AI architecture discussions
- Managing time-boxed events with deep technical teams
- Encouraging participation from introverted data science experts
- Resolving conflicts between model accuracy and delivery speed
- Creating psychological safety in high-pressure AI delivery cycles
Module 13: Architecting for AI at Scale - Designing modular AI systems for continuous delivery
- Defining non-functional requirements for AI services
- Managing technical debt in evolving AI architectures
- Integrating AI models with legacy enterprise systems
- Building scalability into AI inference and training workloads
- Ensuring data privacy and compliance in AI design
- Implementing model interpretability features by design
- Designing for bias detection and mitigation in production
- Aligning AI architecture with security and regulatory standards
- Creating reusable AI components across business units
Module 14: Risk and Compliance Management in AI Delivery - Integrating governance into Agile AI workflows
- Managing regulatory compliance in fast-moving AI sprints
- Documenting model decisions for audit and review
- Conducting ethical impact assessments for AI features
- Handling data privacy requirements in model development
- Implementing model cards and datasheets for transparency
- Managing third-party AI vendor risks and dependencies
- Creating incident response plans for AI failures
- Aligning AI projects with corporate risk appetite
- Building compliance into automated testing and deployment
Module 15: Real-World Implementation: Case Studies and Simulations - Case study: Financial fraud detection system using SAFe
- Case study: Healthcare diagnostics AI rollout at scale
- Case study: AI-powered customer service transformation
- Simulated PI Planning for an autonomous vehicle AI team
- End-to-end Agile delivery simulation for a recommendation engine
- Responding to data source failure during a sprint
- Managing unexpected model bias discovery in production
- Handling regulatory changes mid-PI
- Re-scoping AI objectives due to infrastructure delays
- Aligning executive strategy with team-level AI delivery
Module 16: Certification Preparation and Career Advancement - Reviewing key SAFe concepts for mastery assessment
- Practicing scenario-based questions for real-world application
- Mapping your experience to certification competencies
- Preparing your personal Agile leadership narrative
- Adding the Certificate of Completion to your professional profile
- Leveraging your credential in performance reviews and promotions
- Optimizing LinkedIn and resume language for Agile AI roles
- Networking with other certified professionals globally
- Accessing continued learning pathways from The Art of Service
- Planning your next career move with certified credibility
- Running effective stand-ups with remote AI research teams
- Facilitating backlog refinement for AI experimentation sprints
- Leading iteration planning with probabilistic model outcomes
- Conducting retrospectives that improve AI team dynamics
- Adapting SAFe ceremonies for asynchronous global collaboration
- Using visual facilitation tools for complex AI architecture discussions
- Managing time-boxed events with deep technical teams
- Encouraging participation from introverted data science experts
- Resolving conflicts between model accuracy and delivery speed
- Creating psychological safety in high-pressure AI delivery cycles
Module 13: Architecting for AI at Scale - Designing modular AI systems for continuous delivery
- Defining non-functional requirements for AI services
- Managing technical debt in evolving AI architectures
- Integrating AI models with legacy enterprise systems
- Building scalability into AI inference and training workloads
- Ensuring data privacy and compliance in AI design
- Implementing model interpretability features by design
- Designing for bias detection and mitigation in production
- Aligning AI architecture with security and regulatory standards
- Creating reusable AI components across business units
Module 14: Risk and Compliance Management in AI Delivery - Integrating governance into Agile AI workflows
- Managing regulatory compliance in fast-moving AI sprints
- Documenting model decisions for audit and review
- Conducting ethical impact assessments for AI features
- Handling data privacy requirements in model development
- Implementing model cards and datasheets for transparency
- Managing third-party AI vendor risks and dependencies
- Creating incident response plans for AI failures
- Aligning AI projects with corporate risk appetite
- Building compliance into automated testing and deployment
Module 15: Real-World Implementation: Case Studies and Simulations - Case study: Financial fraud detection system using SAFe
- Case study: Healthcare diagnostics AI rollout at scale
- Case study: AI-powered customer service transformation
- Simulated PI Planning for an autonomous vehicle AI team
- End-to-end Agile delivery simulation for a recommendation engine
- Responding to data source failure during a sprint
- Managing unexpected model bias discovery in production
- Handling regulatory changes mid-PI
- Re-scoping AI objectives due to infrastructure delays
- Aligning executive strategy with team-level AI delivery
Module 16: Certification Preparation and Career Advancement - Reviewing key SAFe concepts for mastery assessment
- Practicing scenario-based questions for real-world application
- Mapping your experience to certification competencies
- Preparing your personal Agile leadership narrative
- Adding the Certificate of Completion to your professional profile
- Leveraging your credential in performance reviews and promotions
- Optimizing LinkedIn and resume language for Agile AI roles
- Networking with other certified professionals globally
- Accessing continued learning pathways from The Art of Service
- Planning your next career move with certified credibility
- Integrating governance into Agile AI workflows
- Managing regulatory compliance in fast-moving AI sprints
- Documenting model decisions for audit and review
- Conducting ethical impact assessments for AI features
- Handling data privacy requirements in model development
- Implementing model cards and datasheets for transparency
- Managing third-party AI vendor risks and dependencies
- Creating incident response plans for AI failures
- Aligning AI projects with corporate risk appetite
- Building compliance into automated testing and deployment
Module 15: Real-World Implementation: Case Studies and Simulations - Case study: Financial fraud detection system using SAFe
- Case study: Healthcare diagnostics AI rollout at scale
- Case study: AI-powered customer service transformation
- Simulated PI Planning for an autonomous vehicle AI team
- End-to-end Agile delivery simulation for a recommendation engine
- Responding to data source failure during a sprint
- Managing unexpected model bias discovery in production
- Handling regulatory changes mid-PI
- Re-scoping AI objectives due to infrastructure delays
- Aligning executive strategy with team-level AI delivery
Module 16: Certification Preparation and Career Advancement - Reviewing key SAFe concepts for mastery assessment
- Practicing scenario-based questions for real-world application
- Mapping your experience to certification competencies
- Preparing your personal Agile leadership narrative
- Adding the Certificate of Completion to your professional profile
- Leveraging your credential in performance reviews and promotions
- Optimizing LinkedIn and resume language for Agile AI roles
- Networking with other certified professionals globally
- Accessing continued learning pathways from The Art of Service
- Planning your next career move with certified credibility
- Reviewing key SAFe concepts for mastery assessment
- Practicing scenario-based questions for real-world application
- Mapping your experience to certification competencies
- Preparing your personal Agile leadership narrative
- Adding the Certificate of Completion to your professional profile
- Leveraging your credential in performance reviews and promotions
- Optimizing LinkedIn and resume language for Agile AI roles
- Networking with other certified professionals globally
- Accessing continued learning pathways from The Art of Service
- Planning your next career move with certified credibility