Mastering Cloud Native DevOps for AI-Driven Enterprises
Course Format & Delivery Details Self-Paced. Immediate Online Access. Lifetime Updates. Risk-Free.
This course is designed for professionals who demand flexibility without sacrificing depth, structure, or support. From the moment you enroll, you gain secure, 24/7 global access to a meticulously structured learning platform built for tomorrow’s cloud architects, DevOps engineers, and AI operations leaders. The entire experience is self-paced, on-demand, and optimized for maximum retention and real-world application-no fixed schedules, no time zones, no attendance tracking. You control when, where, and how fast you learn. Most learners complete the program in 12 to 16 weeks while working full time, dedicating 6 to 8 hours per week. However, many report implementing high-impact practices within the first two modules, accelerating their ability to contribute meaningfully to cloud-native AI infrastructure initiatives immediately. Lifetime Access. Zero Expiry. Always Up to Date.
Once enrolled, you receive permanent access to all course materials-including every future update at no additional cost. The field of cloud-native DevOps evolves rapidly, and so does this course. As new patterns emerge in Kubernetes orchestration, AI model deployment pipelines, or secure service mesh configurations, the curriculum is continuously refined and expanded. Your investment today protects your expertise for years to come. Learn Anywhere, On Any Device
The full course platform is mobile-friendly and accessible across desktops, tablets, and smartphones. Seamless synchronization ensures you can start a deep dive on your laptop during work hours and continue on your phone during transit-progress is tracked automatically, regardless of device. Direct Instructor Guidance & Proven Support Framework
You are not learning in isolation. Throughout the course, you have structured access to expert-led guidance through curated Q&A workflows, scenario-based feedback loops, and tactical troubleshooting frameworks. Our support model is built on responsiveness and clarity-expect detailed, role-specific insights from instructors with active experience managing AI workloads at enterprise scale in AWS, Azure, and GCP environments. Verified Certificate of Completion – Issued by The Art of Service
Upon successful completion, you earn a Certificate of Completion issued by The Art of Service, an internationally recognized provider of professional development programs trusted by Fortune 500 teams, government agencies, and leading tech consultancies worldwide. This certification validates your mastery of cloud-native DevOps principles applied specifically to AI-driven architectures. It is shareable on LinkedIn, included in resumes, and recognized by hiring managers as a signal of applied technical rigor and systems thinking. No Hidden Fees. Transparent, One-Time Pricing.
The total cost is straightforward and all-inclusive. There are no recurring charges, upsells, or surprise fees. Payment grants full access to all modules, resources, project templates, and certification-nothing is locked behind additional paywalls. We accept all major payment methods including Visa, Mastercard, and PayPal. 100% Money-Back Guarantee – Zero Risk Enrollment
If at any point within 30 days you find the course does not meet your expectations for depth, relevance, or career impact, simply request a full refund. No questions asked. No forms to complete. This promise eliminates risk and reflects our absolute confidence in the transformative value of the program. Enrollment Confirmation & Access Process
After completing your enrollment, you will receive an email confirmation of your registration. Your access credentials and entry instructions will be delivered separately once your course materials are prepared and ready for optimal engagement. This ensures a seamless start to your learning journey. This Works Even If…
You’ve tried other DevOps courses that felt too theoretical. You work in a hybrid environment with legacy integrations. Your company hasn’t fully adopted Kubernetes yet. You’re transitioning from traditional IT into AI infrastructure roles. You’ve never managed model deployment pipelines before. You’re unsure if cloud-native patterns apply to your industry. This works even if you’re starting from partial experience. The curriculum is engineered for professionals across experience levels-from senior systems engineers adapting to AI workflows to platform architects redesigning CI/CD for machine learning operations. Every concept is rooted in real enterprise challenges, not hypotheticals. Real Results, From Real Professionals
- I went from managing standard microservices to leading our company’s MLOps transformation within three months of starting this course. The structured approach to GitOps for AI models gave me the confidence to propose a new deployment strategy that cut model rollback time by 68%. – Rafael M, Site Reliability Engineer, Financial Services
- As a solutions architect in healthcare, I needed to ensure HIPAA-compliant AI deployments without sacrificing agility. This course delivered precise frameworks for secure, auditable pipelines. I now train others in my organization using the exact methodologies taught here. – Naomi L, Cloud Solutions Architect
- he hands-on labs on Istio integration with TensorFlow Serving were exactly what I needed to solve latency issues in our production inference layer. I implemented changes the same week and saw a 40% improvement in response consistency. – Dev P, AI Infrastructure Lead
Overcome the “Will This Work For Me?” Objection
We understand that time is your most valuable resource. That’s why every module includes role-specific implementation paths-for DevOps engineers, platform leads, SREs, MLOps specialists, and cloud architects. Whether you're deploying large language models at scale or optimizing CI/CD for computer vision workloads, the content adapts to your context. Combined with proven frameworks, documented troubleshooting workflows, and direct applicability to real production systems, this course eliminates guesswork. You get clarity. You get structure. You get results. Maximum Career ROI. Minimum Friction.
With lifetime access, global availability, tangible career outcomes, and a risk-free enrollment promise, every element of this course is designed to increase your value in the market. You're not just learning tools-you're mastering strategies that align AI innovation with enterprise-grade reliability, security, and speed. The result? Faster promotions, higher-impact projects, and recognition as a technical leader in one of the most in-demand disciplines of the decade.
Extensive and Detailed Course Curriculum
Module 1: Foundations of Cloud-Native Systems for AI Workloads - Understanding the shift from monolithic to cloud-native architectures
- Defining AI-driven enterprise requirements for scalability and resilience
- Core principles of microservices in machine learning environments
- Stateless vs stateful services in AI inference and training pipelines
- Designing for failure tolerance in distributed AI systems
- Decentralized data management strategies for model training
- Service boundaries and domain-driven design in AI platforms
- Event-driven communication patterns for real-time model feedback
- Fault isolation techniques in large-scale AI deployments
- Latency budgeting for real-time AI inference services
- Multi-tenancy considerations in shared AI infrastructure
- Versioning strategies for models, APIs, and service contracts
- Capacity planning for unpredictable AI workload bursts
- Cost-aware design principles for cloud-native AI systems
- Security by design in AI service communication
- Observability foundations in cloud-native environments
Module 2: Containerization and Orchestration Fundamentals - Building minimal container images for AI model serving
- Optimizing Dockerfile structure for reproducible ML environments
- Multi-stage builds for training, testing, and deployment containers
- Container security scanning and vulnerability mitigation
- Image registry management in private and public clouds
- Runtime constraints and resource limits for GPU-enabled containers
- Networking models in containerized AI applications
- Service discovery patterns for dynamic AI workloads
- Introduction to Kubernetes architecture and components
- Deploying stateful sets for model parameter storage
- Managing persistent volumes for checkpoint data
- Node affinity and taints for hardware-specific AI tasks
- Rolling updates and canary deployments for AI services
- Health checks and readiness probes in model inference pods
- Scaling strategies for variable AI traffic loads
- Namespace organization for AI project isolation
Module 3: Advanced Kubernetes for AI Operations - Custom Resource Definitions for AI model lifecycle management
- Building controllers for automated model retraining
- Operator patterns for managing AI pipeline operators
- Kubernetes cluster federation for global AI deployment
- Multi-cluster workload distribution for disaster recovery
- Cluster autoscaling based on AI job queue depth
- GPU resource sharing and quota enforcement
- Kube-batch and Volcano for AI job scheduling
- Integration with NVIDIA GPU operators
- Monitoring GPU utilization across model workloads
- Node pools optimized for inference versus training workloads
- Spot instance strategies for cost-effective batch inference
- Pod disruption budgets in high-availability AI services
- Priority classes for critical model deployment jobs
- Topology spread constraints for geo-distributed AI systems
- Cluster API for infrastructure-as-code provisioning
Module 4: CI/CD Pipelines for Machine Learning Systems - Designing model development workflows with version control
- Data versioning with DVC and LakeFS integration
- Model registry setup and governance policies
- Automated testing frameworks for model accuracy and fairness
- Drift detection pipelines in production models
- Canary analysis for model performance regression
- Blue-green deployments for AI inference endpoints
- Rollback automation based on model health signals
- Parameter sweeping and hyperparameter tracking systems
- Model packaging standards for deployment portability
- Environment parity across development, staging, and production
- Automated documentation generation for AI pipelines
- Secrets management in CI/CD workflows
- End-to-end traceability of model lineage
- Approval gates for high-risk model updates
- Audit logging for regulatory compliance in AI systems
Module 5: GitOps and Infrastructure as Code for AI Platforms - Core principles of GitOps in cloud-native operations
- FluxCD and ArgoCD for declarative AI infrastructure
- Synchronizing cluster state with Git repositories
- Pull-based deployment models for regulated environments
- Policy enforcement with Open Policy Agent
- Infrastructure-as-code with Terraform for AI clusters
- Module reuse and composition in cloud provisioning
- Managing state securely in remote backends
- Cross-cloud deployment strategies using IaC
- Automated drift detection and reconciliation
- Git-based rollback mechanisms for configuration failures
- Environment templating for consistent AI staging
- RBAC configuration through code
- Secure secret injection using SOPS and SealedSecrets
- Automated compliance checks in pull requests
- Cost estimation previews before infrastructure apply
Module 6: Observability in AI-Enabled Systems - Distributed tracing for AI model request flows
- Metrics collection from model inference endpoints
- Structured logging in Python and TensorFlow serving
- Correlating model input data with performance metrics
- Setting SLOs and error budgets for AI services
- Alerting strategies for model degradation
- Monitoring model prediction latency distributions
- Detecting cold start issues in serverless AI functions
- Profiling CPU and memory usage in inference containers
- Custom dashboards for AI operations KPIs
- Real-time monitoring of data pipeline health
- Log aggregation at scale using Loki and Grafana
- Exporting telemetry to centralized AI observability platforms
- Correlation of infrastructure metrics with model behavior
- Root cause analysis workflows for service degradation
- Benchmarking AI system performance over time
Module 7: Security and Compliance in AI Infrastructure - Zero-trust architecture for AI microservices
- Network policies for service-to-service communication
- mTLS enforcement with service mesh integration
- Identity and access management for model APIs
- Just-in-time access controls for production systems
- Role-based access control in Kubernetes for AI teams
- Pod security policies and admission controllers
- Runtime security with Falco and Sysdig
- Compliance frameworks for AI in regulated industries
- Data masking and anonymization in training pipelines
- Encryption of model weights and sensitive parameters
- Audit trails for model decision provenance
- Secure model export and sharing protocols
- Penetration testing strategies for AI endpoints
- Hardening container images for production AI
- Third-party dependency scanning for ML libraries
Module 8: Service Mesh and API Management for AI Services - Introduction to Istio and Linkerd for AI traffic control
- Sidecar proxy configuration for model servers
- Dynamic routing for versioned AI models
- Request mirroring for A/B testing workflows
- Rate limiting and quota management for API consumers
- Circuit breaking to protect overloaded inference services
- Retries and timeouts in AI service communication
- JWT authentication for model API endpoints
- Fine-grained access policies based on user roles
- Telemetry generation from service mesh proxies
- Fault injection for resilience testing in AI systems
- Service-level objective enforcement via policies
- Multi-cluster service mesh topologies
- Integration with API gateways for external access
- GraphQL support for flexible AI query interfaces
- API documentation automation with OpenAPI standards
Module 9: Scaling AI Workloads with Serverless and Batch Systems - Serverless computing models for event-driven AI tasks
- Function as a Service with Knative and OpenFaaS
- Auto-scaling based on message queue depth
- Batch processing frameworks for large-scale inference
- KEDA for event-driven Kubernetes autoscaling
- Processing streaming data with Apache Kafka and AI
- Time-triggered model retraining workflows
- Scheduling AI jobs with CronJobs and Argo Workflows
- Workflow orchestration with Tekton Pipelines
- Parameterized job execution for hyperparameter sweeps
- Error handling and retry logic in AI batch jobs
- Output collection and aggregation strategies
- Cost-performance tradeoffs in serverless AI
- Memory and timeout constraints in function environments
- State management in ephemeral AI functions
- Monitoring and logging in serverless AI deployments
Module 10: GPU and Accelerator Management in Production - NVIDIA GPU provisioning in Kubernetes clusters
- Device plugins and extended resources in K8s
- Monitoring GPU memory and utilization metrics
- Scheduling AI jobs to GPU-enabled nodes
- Time-slicing GPUs for multiple model tasks
- Virtual GPU allocation strategies
- Multi-instance GPU (MIG) configuration
- Monitoring tensor core usage in inference
- Thermal and power constraints in dense AI clusters
- Driver management and version compatibility
- Firmware updates for AI accelerators
- Resource quotas for GPU workloads
- Cost allocation by accelerator usage
- Hybrid CPU-GPU workload balancing
- Fault tolerance in GPU node failures
- Benchmarking model performance by hardware type
Module 11: Data Pipelines and Storage for AI Systems - Designing robust data ingestion frameworks
- Streaming vs batch data processing for AI
- Data lakehouse architectures with Delta Lake
- Schema evolution management in AI datasets
- Data partitioning strategies for query performance
- Metadata cataloging with Apache Atlas
- Data lineage tracking across transformations
- Consistency models in distributed AI storage
- Mounting cloud storage in Kubernetes pods
- Caching strategies for frequently accessed model data
- Data retention and lifecycle policies
- Backup and restore procedures for AI datasets
- Encryption of data at rest and in transit
- Access control for sensitive training data
- Data quality validation pipelines
- Anomaly detection in incoming AI data streams
Module 12: MLOps Frameworks and Tooling Ecosystems - Comparative analysis of Kubeflow, MLflow, and SageMaker
- Setting up a unified MLOps control plane
- Model versioning and experiment tracking
- Feature store implementation with Feast or Tecton
- Online vs offline feature serving patterns
- Feature consistency across training and inference
- Model monitoring for prediction skew and drift
- Automated retraining triggers based on data drift
- Model performance dashboards and alerts
- Model interpretability and explainability tools
- Fairness, bias, and ethical AI monitoring
- Regulatory compliance tooling for AI governance
- Integration with enterprise data warehouses
- Unified logging across training, testing, and deployment
- End-to-end automation from data to deployment
- Vendor-agnostic tool selection strategies
Module 13: Real-World Implementation Projects - Designing a cloud-native AI platform for financial fraud detection
- Implementing CI/CD for a computer vision pipeline
- Building a self-healing model deployment system
- Creating a multi-region inference cluster for global users
- Setting up automated compliance checks for healthcare AI
- Optimizing inference latency in a recommendation engine
- Developing a GPU-sharing policy for research teams
- Deploying a serverless LLM summarization service
- Constructing a real-time anomaly detection architecture
- Integrating audit trails into model decision pipelines
- Automating model certification workflows
- Implementing zero-downtime model updates
- Building a disaster recovery plan for AI infrastructure
- Creating observability dashboards for executive reporting
- Scaling a speech recognition service during peak load
- Hardening an AI API for public exposure
Module 14: Integration with Enterprise Systems and Governance - Aligning AI DevOps with ITIL change management
- Integrating with enterprise monitoring suites
- Active directory integration for team access
- Single sign-on for AI platform dashboards
- Change advisory board (CAB) workflows for AI deployments
- Release calendar coordination across teams
- Service catalog registration for AI capabilities
- Disaster recovery testing for AI systems
- Business continuity planning for model services
- Capacity reporting for cloud cost centers
- Budget forecasting for AI infrastructure
- Vendor contract management for cloud AI services
- Internal SLA definition for AI team deliverables
- Knowledge transfer documentation standards
- Onboarding checklists for new AI engineers
- Audit preparation for SOX, HIPAA, or GDPR compliance
Module 15: Certification Preparation & Next Career Steps - Review of key cloud-native DevOps concepts for AI
- Practice scenarios for real-world troubleshooting
- Common pitfalls in AI infrastructure design
- Architectural decision records for complex systems
- Documentation best practices for production AI
- Presenting technical designs to cross-functional teams
- Communicating risk and tradeoffs to leadership
- Resume optimization for cloud-native AI roles
- LinkedIn profile enhancement with certification
- Interview preparation for DevOps and MLOps roles
- Negotiating technical leadership opportunities
- Building a personal brand in AI infrastructure
- Contributing to open-source projects in MLOps
- Speaking at industry conferences and meetups
- Mentoring junior engineers in cloud-native patterns
- Transitioning to AI platform architecture or engineering management
Module 1: Foundations of Cloud-Native Systems for AI Workloads - Understanding the shift from monolithic to cloud-native architectures
- Defining AI-driven enterprise requirements for scalability and resilience
- Core principles of microservices in machine learning environments
- Stateless vs stateful services in AI inference and training pipelines
- Designing for failure tolerance in distributed AI systems
- Decentralized data management strategies for model training
- Service boundaries and domain-driven design in AI platforms
- Event-driven communication patterns for real-time model feedback
- Fault isolation techniques in large-scale AI deployments
- Latency budgeting for real-time AI inference services
- Multi-tenancy considerations in shared AI infrastructure
- Versioning strategies for models, APIs, and service contracts
- Capacity planning for unpredictable AI workload bursts
- Cost-aware design principles for cloud-native AI systems
- Security by design in AI service communication
- Observability foundations in cloud-native environments
Module 2: Containerization and Orchestration Fundamentals - Building minimal container images for AI model serving
- Optimizing Dockerfile structure for reproducible ML environments
- Multi-stage builds for training, testing, and deployment containers
- Container security scanning and vulnerability mitigation
- Image registry management in private and public clouds
- Runtime constraints and resource limits for GPU-enabled containers
- Networking models in containerized AI applications
- Service discovery patterns for dynamic AI workloads
- Introduction to Kubernetes architecture and components
- Deploying stateful sets for model parameter storage
- Managing persistent volumes for checkpoint data
- Node affinity and taints for hardware-specific AI tasks
- Rolling updates and canary deployments for AI services
- Health checks and readiness probes in model inference pods
- Scaling strategies for variable AI traffic loads
- Namespace organization for AI project isolation
Module 3: Advanced Kubernetes for AI Operations - Custom Resource Definitions for AI model lifecycle management
- Building controllers for automated model retraining
- Operator patterns for managing AI pipeline operators
- Kubernetes cluster federation for global AI deployment
- Multi-cluster workload distribution for disaster recovery
- Cluster autoscaling based on AI job queue depth
- GPU resource sharing and quota enforcement
- Kube-batch and Volcano for AI job scheduling
- Integration with NVIDIA GPU operators
- Monitoring GPU utilization across model workloads
- Node pools optimized for inference versus training workloads
- Spot instance strategies for cost-effective batch inference
- Pod disruption budgets in high-availability AI services
- Priority classes for critical model deployment jobs
- Topology spread constraints for geo-distributed AI systems
- Cluster API for infrastructure-as-code provisioning
Module 4: CI/CD Pipelines for Machine Learning Systems - Designing model development workflows with version control
- Data versioning with DVC and LakeFS integration
- Model registry setup and governance policies
- Automated testing frameworks for model accuracy and fairness
- Drift detection pipelines in production models
- Canary analysis for model performance regression
- Blue-green deployments for AI inference endpoints
- Rollback automation based on model health signals
- Parameter sweeping and hyperparameter tracking systems
- Model packaging standards for deployment portability
- Environment parity across development, staging, and production
- Automated documentation generation for AI pipelines
- Secrets management in CI/CD workflows
- End-to-end traceability of model lineage
- Approval gates for high-risk model updates
- Audit logging for regulatory compliance in AI systems
Module 5: GitOps and Infrastructure as Code for AI Platforms - Core principles of GitOps in cloud-native operations
- FluxCD and ArgoCD for declarative AI infrastructure
- Synchronizing cluster state with Git repositories
- Pull-based deployment models for regulated environments
- Policy enforcement with Open Policy Agent
- Infrastructure-as-code with Terraform for AI clusters
- Module reuse and composition in cloud provisioning
- Managing state securely in remote backends
- Cross-cloud deployment strategies using IaC
- Automated drift detection and reconciliation
- Git-based rollback mechanisms for configuration failures
- Environment templating for consistent AI staging
- RBAC configuration through code
- Secure secret injection using SOPS and SealedSecrets
- Automated compliance checks in pull requests
- Cost estimation previews before infrastructure apply
Module 6: Observability in AI-Enabled Systems - Distributed tracing for AI model request flows
- Metrics collection from model inference endpoints
- Structured logging in Python and TensorFlow serving
- Correlating model input data with performance metrics
- Setting SLOs and error budgets for AI services
- Alerting strategies for model degradation
- Monitoring model prediction latency distributions
- Detecting cold start issues in serverless AI functions
- Profiling CPU and memory usage in inference containers
- Custom dashboards for AI operations KPIs
- Real-time monitoring of data pipeline health
- Log aggregation at scale using Loki and Grafana
- Exporting telemetry to centralized AI observability platforms
- Correlation of infrastructure metrics with model behavior
- Root cause analysis workflows for service degradation
- Benchmarking AI system performance over time
Module 7: Security and Compliance in AI Infrastructure - Zero-trust architecture for AI microservices
- Network policies for service-to-service communication
- mTLS enforcement with service mesh integration
- Identity and access management for model APIs
- Just-in-time access controls for production systems
- Role-based access control in Kubernetes for AI teams
- Pod security policies and admission controllers
- Runtime security with Falco and Sysdig
- Compliance frameworks for AI in regulated industries
- Data masking and anonymization in training pipelines
- Encryption of model weights and sensitive parameters
- Audit trails for model decision provenance
- Secure model export and sharing protocols
- Penetration testing strategies for AI endpoints
- Hardening container images for production AI
- Third-party dependency scanning for ML libraries
Module 8: Service Mesh and API Management for AI Services - Introduction to Istio and Linkerd for AI traffic control
- Sidecar proxy configuration for model servers
- Dynamic routing for versioned AI models
- Request mirroring for A/B testing workflows
- Rate limiting and quota management for API consumers
- Circuit breaking to protect overloaded inference services
- Retries and timeouts in AI service communication
- JWT authentication for model API endpoints
- Fine-grained access policies based on user roles
- Telemetry generation from service mesh proxies
- Fault injection for resilience testing in AI systems
- Service-level objective enforcement via policies
- Multi-cluster service mesh topologies
- Integration with API gateways for external access
- GraphQL support for flexible AI query interfaces
- API documentation automation with OpenAPI standards
Module 9: Scaling AI Workloads with Serverless and Batch Systems - Serverless computing models for event-driven AI tasks
- Function as a Service with Knative and OpenFaaS
- Auto-scaling based on message queue depth
- Batch processing frameworks for large-scale inference
- KEDA for event-driven Kubernetes autoscaling
- Processing streaming data with Apache Kafka and AI
- Time-triggered model retraining workflows
- Scheduling AI jobs with CronJobs and Argo Workflows
- Workflow orchestration with Tekton Pipelines
- Parameterized job execution for hyperparameter sweeps
- Error handling and retry logic in AI batch jobs
- Output collection and aggregation strategies
- Cost-performance tradeoffs in serverless AI
- Memory and timeout constraints in function environments
- State management in ephemeral AI functions
- Monitoring and logging in serverless AI deployments
Module 10: GPU and Accelerator Management in Production - NVIDIA GPU provisioning in Kubernetes clusters
- Device plugins and extended resources in K8s
- Monitoring GPU memory and utilization metrics
- Scheduling AI jobs to GPU-enabled nodes
- Time-slicing GPUs for multiple model tasks
- Virtual GPU allocation strategies
- Multi-instance GPU (MIG) configuration
- Monitoring tensor core usage in inference
- Thermal and power constraints in dense AI clusters
- Driver management and version compatibility
- Firmware updates for AI accelerators
- Resource quotas for GPU workloads
- Cost allocation by accelerator usage
- Hybrid CPU-GPU workload balancing
- Fault tolerance in GPU node failures
- Benchmarking model performance by hardware type
Module 11: Data Pipelines and Storage for AI Systems - Designing robust data ingestion frameworks
- Streaming vs batch data processing for AI
- Data lakehouse architectures with Delta Lake
- Schema evolution management in AI datasets
- Data partitioning strategies for query performance
- Metadata cataloging with Apache Atlas
- Data lineage tracking across transformations
- Consistency models in distributed AI storage
- Mounting cloud storage in Kubernetes pods
- Caching strategies for frequently accessed model data
- Data retention and lifecycle policies
- Backup and restore procedures for AI datasets
- Encryption of data at rest and in transit
- Access control for sensitive training data
- Data quality validation pipelines
- Anomaly detection in incoming AI data streams
Module 12: MLOps Frameworks and Tooling Ecosystems - Comparative analysis of Kubeflow, MLflow, and SageMaker
- Setting up a unified MLOps control plane
- Model versioning and experiment tracking
- Feature store implementation with Feast or Tecton
- Online vs offline feature serving patterns
- Feature consistency across training and inference
- Model monitoring for prediction skew and drift
- Automated retraining triggers based on data drift
- Model performance dashboards and alerts
- Model interpretability and explainability tools
- Fairness, bias, and ethical AI monitoring
- Regulatory compliance tooling for AI governance
- Integration with enterprise data warehouses
- Unified logging across training, testing, and deployment
- End-to-end automation from data to deployment
- Vendor-agnostic tool selection strategies
Module 13: Real-World Implementation Projects - Designing a cloud-native AI platform for financial fraud detection
- Implementing CI/CD for a computer vision pipeline
- Building a self-healing model deployment system
- Creating a multi-region inference cluster for global users
- Setting up automated compliance checks for healthcare AI
- Optimizing inference latency in a recommendation engine
- Developing a GPU-sharing policy for research teams
- Deploying a serverless LLM summarization service
- Constructing a real-time anomaly detection architecture
- Integrating audit trails into model decision pipelines
- Automating model certification workflows
- Implementing zero-downtime model updates
- Building a disaster recovery plan for AI infrastructure
- Creating observability dashboards for executive reporting
- Scaling a speech recognition service during peak load
- Hardening an AI API for public exposure
Module 14: Integration with Enterprise Systems and Governance - Aligning AI DevOps with ITIL change management
- Integrating with enterprise monitoring suites
- Active directory integration for team access
- Single sign-on for AI platform dashboards
- Change advisory board (CAB) workflows for AI deployments
- Release calendar coordination across teams
- Service catalog registration for AI capabilities
- Disaster recovery testing for AI systems
- Business continuity planning for model services
- Capacity reporting for cloud cost centers
- Budget forecasting for AI infrastructure
- Vendor contract management for cloud AI services
- Internal SLA definition for AI team deliverables
- Knowledge transfer documentation standards
- Onboarding checklists for new AI engineers
- Audit preparation for SOX, HIPAA, or GDPR compliance
Module 15: Certification Preparation & Next Career Steps - Review of key cloud-native DevOps concepts for AI
- Practice scenarios for real-world troubleshooting
- Common pitfalls in AI infrastructure design
- Architectural decision records for complex systems
- Documentation best practices for production AI
- Presenting technical designs to cross-functional teams
- Communicating risk and tradeoffs to leadership
- Resume optimization for cloud-native AI roles
- LinkedIn profile enhancement with certification
- Interview preparation for DevOps and MLOps roles
- Negotiating technical leadership opportunities
- Building a personal brand in AI infrastructure
- Contributing to open-source projects in MLOps
- Speaking at industry conferences and meetups
- Mentoring junior engineers in cloud-native patterns
- Transitioning to AI platform architecture or engineering management
- Building minimal container images for AI model serving
- Optimizing Dockerfile structure for reproducible ML environments
- Multi-stage builds for training, testing, and deployment containers
- Container security scanning and vulnerability mitigation
- Image registry management in private and public clouds
- Runtime constraints and resource limits for GPU-enabled containers
- Networking models in containerized AI applications
- Service discovery patterns for dynamic AI workloads
- Introduction to Kubernetes architecture and components
- Deploying stateful sets for model parameter storage
- Managing persistent volumes for checkpoint data
- Node affinity and taints for hardware-specific AI tasks
- Rolling updates and canary deployments for AI services
- Health checks and readiness probes in model inference pods
- Scaling strategies for variable AI traffic loads
- Namespace organization for AI project isolation
Module 3: Advanced Kubernetes for AI Operations - Custom Resource Definitions for AI model lifecycle management
- Building controllers for automated model retraining
- Operator patterns for managing AI pipeline operators
- Kubernetes cluster federation for global AI deployment
- Multi-cluster workload distribution for disaster recovery
- Cluster autoscaling based on AI job queue depth
- GPU resource sharing and quota enforcement
- Kube-batch and Volcano for AI job scheduling
- Integration with NVIDIA GPU operators
- Monitoring GPU utilization across model workloads
- Node pools optimized for inference versus training workloads
- Spot instance strategies for cost-effective batch inference
- Pod disruption budgets in high-availability AI services
- Priority classes for critical model deployment jobs
- Topology spread constraints for geo-distributed AI systems
- Cluster API for infrastructure-as-code provisioning
Module 4: CI/CD Pipelines for Machine Learning Systems - Designing model development workflows with version control
- Data versioning with DVC and LakeFS integration
- Model registry setup and governance policies
- Automated testing frameworks for model accuracy and fairness
- Drift detection pipelines in production models
- Canary analysis for model performance regression
- Blue-green deployments for AI inference endpoints
- Rollback automation based on model health signals
- Parameter sweeping and hyperparameter tracking systems
- Model packaging standards for deployment portability
- Environment parity across development, staging, and production
- Automated documentation generation for AI pipelines
- Secrets management in CI/CD workflows
- End-to-end traceability of model lineage
- Approval gates for high-risk model updates
- Audit logging for regulatory compliance in AI systems
Module 5: GitOps and Infrastructure as Code for AI Platforms - Core principles of GitOps in cloud-native operations
- FluxCD and ArgoCD for declarative AI infrastructure
- Synchronizing cluster state with Git repositories
- Pull-based deployment models for regulated environments
- Policy enforcement with Open Policy Agent
- Infrastructure-as-code with Terraform for AI clusters
- Module reuse and composition in cloud provisioning
- Managing state securely in remote backends
- Cross-cloud deployment strategies using IaC
- Automated drift detection and reconciliation
- Git-based rollback mechanisms for configuration failures
- Environment templating for consistent AI staging
- RBAC configuration through code
- Secure secret injection using SOPS and SealedSecrets
- Automated compliance checks in pull requests
- Cost estimation previews before infrastructure apply
Module 6: Observability in AI-Enabled Systems - Distributed tracing for AI model request flows
- Metrics collection from model inference endpoints
- Structured logging in Python and TensorFlow serving
- Correlating model input data with performance metrics
- Setting SLOs and error budgets for AI services
- Alerting strategies for model degradation
- Monitoring model prediction latency distributions
- Detecting cold start issues in serverless AI functions
- Profiling CPU and memory usage in inference containers
- Custom dashboards for AI operations KPIs
- Real-time monitoring of data pipeline health
- Log aggregation at scale using Loki and Grafana
- Exporting telemetry to centralized AI observability platforms
- Correlation of infrastructure metrics with model behavior
- Root cause analysis workflows for service degradation
- Benchmarking AI system performance over time
Module 7: Security and Compliance in AI Infrastructure - Zero-trust architecture for AI microservices
- Network policies for service-to-service communication
- mTLS enforcement with service mesh integration
- Identity and access management for model APIs
- Just-in-time access controls for production systems
- Role-based access control in Kubernetes for AI teams
- Pod security policies and admission controllers
- Runtime security with Falco and Sysdig
- Compliance frameworks for AI in regulated industries
- Data masking and anonymization in training pipelines
- Encryption of model weights and sensitive parameters
- Audit trails for model decision provenance
- Secure model export and sharing protocols
- Penetration testing strategies for AI endpoints
- Hardening container images for production AI
- Third-party dependency scanning for ML libraries
Module 8: Service Mesh and API Management for AI Services - Introduction to Istio and Linkerd for AI traffic control
- Sidecar proxy configuration for model servers
- Dynamic routing for versioned AI models
- Request mirroring for A/B testing workflows
- Rate limiting and quota management for API consumers
- Circuit breaking to protect overloaded inference services
- Retries and timeouts in AI service communication
- JWT authentication for model API endpoints
- Fine-grained access policies based on user roles
- Telemetry generation from service mesh proxies
- Fault injection for resilience testing in AI systems
- Service-level objective enforcement via policies
- Multi-cluster service mesh topologies
- Integration with API gateways for external access
- GraphQL support for flexible AI query interfaces
- API documentation automation with OpenAPI standards
Module 9: Scaling AI Workloads with Serverless and Batch Systems - Serverless computing models for event-driven AI tasks
- Function as a Service with Knative and OpenFaaS
- Auto-scaling based on message queue depth
- Batch processing frameworks for large-scale inference
- KEDA for event-driven Kubernetes autoscaling
- Processing streaming data with Apache Kafka and AI
- Time-triggered model retraining workflows
- Scheduling AI jobs with CronJobs and Argo Workflows
- Workflow orchestration with Tekton Pipelines
- Parameterized job execution for hyperparameter sweeps
- Error handling and retry logic in AI batch jobs
- Output collection and aggregation strategies
- Cost-performance tradeoffs in serverless AI
- Memory and timeout constraints in function environments
- State management in ephemeral AI functions
- Monitoring and logging in serverless AI deployments
Module 10: GPU and Accelerator Management in Production - NVIDIA GPU provisioning in Kubernetes clusters
- Device plugins and extended resources in K8s
- Monitoring GPU memory and utilization metrics
- Scheduling AI jobs to GPU-enabled nodes
- Time-slicing GPUs for multiple model tasks
- Virtual GPU allocation strategies
- Multi-instance GPU (MIG) configuration
- Monitoring tensor core usage in inference
- Thermal and power constraints in dense AI clusters
- Driver management and version compatibility
- Firmware updates for AI accelerators
- Resource quotas for GPU workloads
- Cost allocation by accelerator usage
- Hybrid CPU-GPU workload balancing
- Fault tolerance in GPU node failures
- Benchmarking model performance by hardware type
Module 11: Data Pipelines and Storage for AI Systems - Designing robust data ingestion frameworks
- Streaming vs batch data processing for AI
- Data lakehouse architectures with Delta Lake
- Schema evolution management in AI datasets
- Data partitioning strategies for query performance
- Metadata cataloging with Apache Atlas
- Data lineage tracking across transformations
- Consistency models in distributed AI storage
- Mounting cloud storage in Kubernetes pods
- Caching strategies for frequently accessed model data
- Data retention and lifecycle policies
- Backup and restore procedures for AI datasets
- Encryption of data at rest and in transit
- Access control for sensitive training data
- Data quality validation pipelines
- Anomaly detection in incoming AI data streams
Module 12: MLOps Frameworks and Tooling Ecosystems - Comparative analysis of Kubeflow, MLflow, and SageMaker
- Setting up a unified MLOps control plane
- Model versioning and experiment tracking
- Feature store implementation with Feast or Tecton
- Online vs offline feature serving patterns
- Feature consistency across training and inference
- Model monitoring for prediction skew and drift
- Automated retraining triggers based on data drift
- Model performance dashboards and alerts
- Model interpretability and explainability tools
- Fairness, bias, and ethical AI monitoring
- Regulatory compliance tooling for AI governance
- Integration with enterprise data warehouses
- Unified logging across training, testing, and deployment
- End-to-end automation from data to deployment
- Vendor-agnostic tool selection strategies
Module 13: Real-World Implementation Projects - Designing a cloud-native AI platform for financial fraud detection
- Implementing CI/CD for a computer vision pipeline
- Building a self-healing model deployment system
- Creating a multi-region inference cluster for global users
- Setting up automated compliance checks for healthcare AI
- Optimizing inference latency in a recommendation engine
- Developing a GPU-sharing policy for research teams
- Deploying a serverless LLM summarization service
- Constructing a real-time anomaly detection architecture
- Integrating audit trails into model decision pipelines
- Automating model certification workflows
- Implementing zero-downtime model updates
- Building a disaster recovery plan for AI infrastructure
- Creating observability dashboards for executive reporting
- Scaling a speech recognition service during peak load
- Hardening an AI API for public exposure
Module 14: Integration with Enterprise Systems and Governance - Aligning AI DevOps with ITIL change management
- Integrating with enterprise monitoring suites
- Active directory integration for team access
- Single sign-on for AI platform dashboards
- Change advisory board (CAB) workflows for AI deployments
- Release calendar coordination across teams
- Service catalog registration for AI capabilities
- Disaster recovery testing for AI systems
- Business continuity planning for model services
- Capacity reporting for cloud cost centers
- Budget forecasting for AI infrastructure
- Vendor contract management for cloud AI services
- Internal SLA definition for AI team deliverables
- Knowledge transfer documentation standards
- Onboarding checklists for new AI engineers
- Audit preparation for SOX, HIPAA, or GDPR compliance
Module 15: Certification Preparation & Next Career Steps - Review of key cloud-native DevOps concepts for AI
- Practice scenarios for real-world troubleshooting
- Common pitfalls in AI infrastructure design
- Architectural decision records for complex systems
- Documentation best practices for production AI
- Presenting technical designs to cross-functional teams
- Communicating risk and tradeoffs to leadership
- Resume optimization for cloud-native AI roles
- LinkedIn profile enhancement with certification
- Interview preparation for DevOps and MLOps roles
- Negotiating technical leadership opportunities
- Building a personal brand in AI infrastructure
- Contributing to open-source projects in MLOps
- Speaking at industry conferences and meetups
- Mentoring junior engineers in cloud-native patterns
- Transitioning to AI platform architecture or engineering management
- Designing model development workflows with version control
- Data versioning with DVC and LakeFS integration
- Model registry setup and governance policies
- Automated testing frameworks for model accuracy and fairness
- Drift detection pipelines in production models
- Canary analysis for model performance regression
- Blue-green deployments for AI inference endpoints
- Rollback automation based on model health signals
- Parameter sweeping and hyperparameter tracking systems
- Model packaging standards for deployment portability
- Environment parity across development, staging, and production
- Automated documentation generation for AI pipelines
- Secrets management in CI/CD workflows
- End-to-end traceability of model lineage
- Approval gates for high-risk model updates
- Audit logging for regulatory compliance in AI systems
Module 5: GitOps and Infrastructure as Code for AI Platforms - Core principles of GitOps in cloud-native operations
- FluxCD and ArgoCD for declarative AI infrastructure
- Synchronizing cluster state with Git repositories
- Pull-based deployment models for regulated environments
- Policy enforcement with Open Policy Agent
- Infrastructure-as-code with Terraform for AI clusters
- Module reuse and composition in cloud provisioning
- Managing state securely in remote backends
- Cross-cloud deployment strategies using IaC
- Automated drift detection and reconciliation
- Git-based rollback mechanisms for configuration failures
- Environment templating for consistent AI staging
- RBAC configuration through code
- Secure secret injection using SOPS and SealedSecrets
- Automated compliance checks in pull requests
- Cost estimation previews before infrastructure apply
Module 6: Observability in AI-Enabled Systems - Distributed tracing for AI model request flows
- Metrics collection from model inference endpoints
- Structured logging in Python and TensorFlow serving
- Correlating model input data with performance metrics
- Setting SLOs and error budgets for AI services
- Alerting strategies for model degradation
- Monitoring model prediction latency distributions
- Detecting cold start issues in serverless AI functions
- Profiling CPU and memory usage in inference containers
- Custom dashboards for AI operations KPIs
- Real-time monitoring of data pipeline health
- Log aggregation at scale using Loki and Grafana
- Exporting telemetry to centralized AI observability platforms
- Correlation of infrastructure metrics with model behavior
- Root cause analysis workflows for service degradation
- Benchmarking AI system performance over time
Module 7: Security and Compliance in AI Infrastructure - Zero-trust architecture for AI microservices
- Network policies for service-to-service communication
- mTLS enforcement with service mesh integration
- Identity and access management for model APIs
- Just-in-time access controls for production systems
- Role-based access control in Kubernetes for AI teams
- Pod security policies and admission controllers
- Runtime security with Falco and Sysdig
- Compliance frameworks for AI in regulated industries
- Data masking and anonymization in training pipelines
- Encryption of model weights and sensitive parameters
- Audit trails for model decision provenance
- Secure model export and sharing protocols
- Penetration testing strategies for AI endpoints
- Hardening container images for production AI
- Third-party dependency scanning for ML libraries
Module 8: Service Mesh and API Management for AI Services - Introduction to Istio and Linkerd for AI traffic control
- Sidecar proxy configuration for model servers
- Dynamic routing for versioned AI models
- Request mirroring for A/B testing workflows
- Rate limiting and quota management for API consumers
- Circuit breaking to protect overloaded inference services
- Retries and timeouts in AI service communication
- JWT authentication for model API endpoints
- Fine-grained access policies based on user roles
- Telemetry generation from service mesh proxies
- Fault injection for resilience testing in AI systems
- Service-level objective enforcement via policies
- Multi-cluster service mesh topologies
- Integration with API gateways for external access
- GraphQL support for flexible AI query interfaces
- API documentation automation with OpenAPI standards
Module 9: Scaling AI Workloads with Serverless and Batch Systems - Serverless computing models for event-driven AI tasks
- Function as a Service with Knative and OpenFaaS
- Auto-scaling based on message queue depth
- Batch processing frameworks for large-scale inference
- KEDA for event-driven Kubernetes autoscaling
- Processing streaming data with Apache Kafka and AI
- Time-triggered model retraining workflows
- Scheduling AI jobs with CronJobs and Argo Workflows
- Workflow orchestration with Tekton Pipelines
- Parameterized job execution for hyperparameter sweeps
- Error handling and retry logic in AI batch jobs
- Output collection and aggregation strategies
- Cost-performance tradeoffs in serverless AI
- Memory and timeout constraints in function environments
- State management in ephemeral AI functions
- Monitoring and logging in serverless AI deployments
Module 10: GPU and Accelerator Management in Production - NVIDIA GPU provisioning in Kubernetes clusters
- Device plugins and extended resources in K8s
- Monitoring GPU memory and utilization metrics
- Scheduling AI jobs to GPU-enabled nodes
- Time-slicing GPUs for multiple model tasks
- Virtual GPU allocation strategies
- Multi-instance GPU (MIG) configuration
- Monitoring tensor core usage in inference
- Thermal and power constraints in dense AI clusters
- Driver management and version compatibility
- Firmware updates for AI accelerators
- Resource quotas for GPU workloads
- Cost allocation by accelerator usage
- Hybrid CPU-GPU workload balancing
- Fault tolerance in GPU node failures
- Benchmarking model performance by hardware type
Module 11: Data Pipelines and Storage for AI Systems - Designing robust data ingestion frameworks
- Streaming vs batch data processing for AI
- Data lakehouse architectures with Delta Lake
- Schema evolution management in AI datasets
- Data partitioning strategies for query performance
- Metadata cataloging with Apache Atlas
- Data lineage tracking across transformations
- Consistency models in distributed AI storage
- Mounting cloud storage in Kubernetes pods
- Caching strategies for frequently accessed model data
- Data retention and lifecycle policies
- Backup and restore procedures for AI datasets
- Encryption of data at rest and in transit
- Access control for sensitive training data
- Data quality validation pipelines
- Anomaly detection in incoming AI data streams
Module 12: MLOps Frameworks and Tooling Ecosystems - Comparative analysis of Kubeflow, MLflow, and SageMaker
- Setting up a unified MLOps control plane
- Model versioning and experiment tracking
- Feature store implementation with Feast or Tecton
- Online vs offline feature serving patterns
- Feature consistency across training and inference
- Model monitoring for prediction skew and drift
- Automated retraining triggers based on data drift
- Model performance dashboards and alerts
- Model interpretability and explainability tools
- Fairness, bias, and ethical AI monitoring
- Regulatory compliance tooling for AI governance
- Integration with enterprise data warehouses
- Unified logging across training, testing, and deployment
- End-to-end automation from data to deployment
- Vendor-agnostic tool selection strategies
Module 13: Real-World Implementation Projects - Designing a cloud-native AI platform for financial fraud detection
- Implementing CI/CD for a computer vision pipeline
- Building a self-healing model deployment system
- Creating a multi-region inference cluster for global users
- Setting up automated compliance checks for healthcare AI
- Optimizing inference latency in a recommendation engine
- Developing a GPU-sharing policy for research teams
- Deploying a serverless LLM summarization service
- Constructing a real-time anomaly detection architecture
- Integrating audit trails into model decision pipelines
- Automating model certification workflows
- Implementing zero-downtime model updates
- Building a disaster recovery plan for AI infrastructure
- Creating observability dashboards for executive reporting
- Scaling a speech recognition service during peak load
- Hardening an AI API for public exposure
Module 14: Integration with Enterprise Systems and Governance - Aligning AI DevOps with ITIL change management
- Integrating with enterprise monitoring suites
- Active directory integration for team access
- Single sign-on for AI platform dashboards
- Change advisory board (CAB) workflows for AI deployments
- Release calendar coordination across teams
- Service catalog registration for AI capabilities
- Disaster recovery testing for AI systems
- Business continuity planning for model services
- Capacity reporting for cloud cost centers
- Budget forecasting for AI infrastructure
- Vendor contract management for cloud AI services
- Internal SLA definition for AI team deliverables
- Knowledge transfer documentation standards
- Onboarding checklists for new AI engineers
- Audit preparation for SOX, HIPAA, or GDPR compliance
Module 15: Certification Preparation & Next Career Steps - Review of key cloud-native DevOps concepts for AI
- Practice scenarios for real-world troubleshooting
- Common pitfalls in AI infrastructure design
- Architectural decision records for complex systems
- Documentation best practices for production AI
- Presenting technical designs to cross-functional teams
- Communicating risk and tradeoffs to leadership
- Resume optimization for cloud-native AI roles
- LinkedIn profile enhancement with certification
- Interview preparation for DevOps and MLOps roles
- Negotiating technical leadership opportunities
- Building a personal brand in AI infrastructure
- Contributing to open-source projects in MLOps
- Speaking at industry conferences and meetups
- Mentoring junior engineers in cloud-native patterns
- Transitioning to AI platform architecture or engineering management
- Distributed tracing for AI model request flows
- Metrics collection from model inference endpoints
- Structured logging in Python and TensorFlow serving
- Correlating model input data with performance metrics
- Setting SLOs and error budgets for AI services
- Alerting strategies for model degradation
- Monitoring model prediction latency distributions
- Detecting cold start issues in serverless AI functions
- Profiling CPU and memory usage in inference containers
- Custom dashboards for AI operations KPIs
- Real-time monitoring of data pipeline health
- Log aggregation at scale using Loki and Grafana
- Exporting telemetry to centralized AI observability platforms
- Correlation of infrastructure metrics with model behavior
- Root cause analysis workflows for service degradation
- Benchmarking AI system performance over time
Module 7: Security and Compliance in AI Infrastructure - Zero-trust architecture for AI microservices
- Network policies for service-to-service communication
- mTLS enforcement with service mesh integration
- Identity and access management for model APIs
- Just-in-time access controls for production systems
- Role-based access control in Kubernetes for AI teams
- Pod security policies and admission controllers
- Runtime security with Falco and Sysdig
- Compliance frameworks for AI in regulated industries
- Data masking and anonymization in training pipelines
- Encryption of model weights and sensitive parameters
- Audit trails for model decision provenance
- Secure model export and sharing protocols
- Penetration testing strategies for AI endpoints
- Hardening container images for production AI
- Third-party dependency scanning for ML libraries
Module 8: Service Mesh and API Management for AI Services - Introduction to Istio and Linkerd for AI traffic control
- Sidecar proxy configuration for model servers
- Dynamic routing for versioned AI models
- Request mirroring for A/B testing workflows
- Rate limiting and quota management for API consumers
- Circuit breaking to protect overloaded inference services
- Retries and timeouts in AI service communication
- JWT authentication for model API endpoints
- Fine-grained access policies based on user roles
- Telemetry generation from service mesh proxies
- Fault injection for resilience testing in AI systems
- Service-level objective enforcement via policies
- Multi-cluster service mesh topologies
- Integration with API gateways for external access
- GraphQL support for flexible AI query interfaces
- API documentation automation with OpenAPI standards
Module 9: Scaling AI Workloads with Serverless and Batch Systems - Serverless computing models for event-driven AI tasks
- Function as a Service with Knative and OpenFaaS
- Auto-scaling based on message queue depth
- Batch processing frameworks for large-scale inference
- KEDA for event-driven Kubernetes autoscaling
- Processing streaming data with Apache Kafka and AI
- Time-triggered model retraining workflows
- Scheduling AI jobs with CronJobs and Argo Workflows
- Workflow orchestration with Tekton Pipelines
- Parameterized job execution for hyperparameter sweeps
- Error handling and retry logic in AI batch jobs
- Output collection and aggregation strategies
- Cost-performance tradeoffs in serverless AI
- Memory and timeout constraints in function environments
- State management in ephemeral AI functions
- Monitoring and logging in serverless AI deployments
Module 10: GPU and Accelerator Management in Production - NVIDIA GPU provisioning in Kubernetes clusters
- Device plugins and extended resources in K8s
- Monitoring GPU memory and utilization metrics
- Scheduling AI jobs to GPU-enabled nodes
- Time-slicing GPUs for multiple model tasks
- Virtual GPU allocation strategies
- Multi-instance GPU (MIG) configuration
- Monitoring tensor core usage in inference
- Thermal and power constraints in dense AI clusters
- Driver management and version compatibility
- Firmware updates for AI accelerators
- Resource quotas for GPU workloads
- Cost allocation by accelerator usage
- Hybrid CPU-GPU workload balancing
- Fault tolerance in GPU node failures
- Benchmarking model performance by hardware type
Module 11: Data Pipelines and Storage for AI Systems - Designing robust data ingestion frameworks
- Streaming vs batch data processing for AI
- Data lakehouse architectures with Delta Lake
- Schema evolution management in AI datasets
- Data partitioning strategies for query performance
- Metadata cataloging with Apache Atlas
- Data lineage tracking across transformations
- Consistency models in distributed AI storage
- Mounting cloud storage in Kubernetes pods
- Caching strategies for frequently accessed model data
- Data retention and lifecycle policies
- Backup and restore procedures for AI datasets
- Encryption of data at rest and in transit
- Access control for sensitive training data
- Data quality validation pipelines
- Anomaly detection in incoming AI data streams
Module 12: MLOps Frameworks and Tooling Ecosystems - Comparative analysis of Kubeflow, MLflow, and SageMaker
- Setting up a unified MLOps control plane
- Model versioning and experiment tracking
- Feature store implementation with Feast or Tecton
- Online vs offline feature serving patterns
- Feature consistency across training and inference
- Model monitoring for prediction skew and drift
- Automated retraining triggers based on data drift
- Model performance dashboards and alerts
- Model interpretability and explainability tools
- Fairness, bias, and ethical AI monitoring
- Regulatory compliance tooling for AI governance
- Integration with enterprise data warehouses
- Unified logging across training, testing, and deployment
- End-to-end automation from data to deployment
- Vendor-agnostic tool selection strategies
Module 13: Real-World Implementation Projects - Designing a cloud-native AI platform for financial fraud detection
- Implementing CI/CD for a computer vision pipeline
- Building a self-healing model deployment system
- Creating a multi-region inference cluster for global users
- Setting up automated compliance checks for healthcare AI
- Optimizing inference latency in a recommendation engine
- Developing a GPU-sharing policy for research teams
- Deploying a serverless LLM summarization service
- Constructing a real-time anomaly detection architecture
- Integrating audit trails into model decision pipelines
- Automating model certification workflows
- Implementing zero-downtime model updates
- Building a disaster recovery plan for AI infrastructure
- Creating observability dashboards for executive reporting
- Scaling a speech recognition service during peak load
- Hardening an AI API for public exposure
Module 14: Integration with Enterprise Systems and Governance - Aligning AI DevOps with ITIL change management
- Integrating with enterprise monitoring suites
- Active directory integration for team access
- Single sign-on for AI platform dashboards
- Change advisory board (CAB) workflows for AI deployments
- Release calendar coordination across teams
- Service catalog registration for AI capabilities
- Disaster recovery testing for AI systems
- Business continuity planning for model services
- Capacity reporting for cloud cost centers
- Budget forecasting for AI infrastructure
- Vendor contract management for cloud AI services
- Internal SLA definition for AI team deliverables
- Knowledge transfer documentation standards
- Onboarding checklists for new AI engineers
- Audit preparation for SOX, HIPAA, or GDPR compliance
Module 15: Certification Preparation & Next Career Steps - Review of key cloud-native DevOps concepts for AI
- Practice scenarios for real-world troubleshooting
- Common pitfalls in AI infrastructure design
- Architectural decision records for complex systems
- Documentation best practices for production AI
- Presenting technical designs to cross-functional teams
- Communicating risk and tradeoffs to leadership
- Resume optimization for cloud-native AI roles
- LinkedIn profile enhancement with certification
- Interview preparation for DevOps and MLOps roles
- Negotiating technical leadership opportunities
- Building a personal brand in AI infrastructure
- Contributing to open-source projects in MLOps
- Speaking at industry conferences and meetups
- Mentoring junior engineers in cloud-native patterns
- Transitioning to AI platform architecture or engineering management
- Introduction to Istio and Linkerd for AI traffic control
- Sidecar proxy configuration for model servers
- Dynamic routing for versioned AI models
- Request mirroring for A/B testing workflows
- Rate limiting and quota management for API consumers
- Circuit breaking to protect overloaded inference services
- Retries and timeouts in AI service communication
- JWT authentication for model API endpoints
- Fine-grained access policies based on user roles
- Telemetry generation from service mesh proxies
- Fault injection for resilience testing in AI systems
- Service-level objective enforcement via policies
- Multi-cluster service mesh topologies
- Integration with API gateways for external access
- GraphQL support for flexible AI query interfaces
- API documentation automation with OpenAPI standards
Module 9: Scaling AI Workloads with Serverless and Batch Systems - Serverless computing models for event-driven AI tasks
- Function as a Service with Knative and OpenFaaS
- Auto-scaling based on message queue depth
- Batch processing frameworks for large-scale inference
- KEDA for event-driven Kubernetes autoscaling
- Processing streaming data with Apache Kafka and AI
- Time-triggered model retraining workflows
- Scheduling AI jobs with CronJobs and Argo Workflows
- Workflow orchestration with Tekton Pipelines
- Parameterized job execution for hyperparameter sweeps
- Error handling and retry logic in AI batch jobs
- Output collection and aggregation strategies
- Cost-performance tradeoffs in serverless AI
- Memory and timeout constraints in function environments
- State management in ephemeral AI functions
- Monitoring and logging in serverless AI deployments
Module 10: GPU and Accelerator Management in Production - NVIDIA GPU provisioning in Kubernetes clusters
- Device plugins and extended resources in K8s
- Monitoring GPU memory and utilization metrics
- Scheduling AI jobs to GPU-enabled nodes
- Time-slicing GPUs for multiple model tasks
- Virtual GPU allocation strategies
- Multi-instance GPU (MIG) configuration
- Monitoring tensor core usage in inference
- Thermal and power constraints in dense AI clusters
- Driver management and version compatibility
- Firmware updates for AI accelerators
- Resource quotas for GPU workloads
- Cost allocation by accelerator usage
- Hybrid CPU-GPU workload balancing
- Fault tolerance in GPU node failures
- Benchmarking model performance by hardware type
Module 11: Data Pipelines and Storage for AI Systems - Designing robust data ingestion frameworks
- Streaming vs batch data processing for AI
- Data lakehouse architectures with Delta Lake
- Schema evolution management in AI datasets
- Data partitioning strategies for query performance
- Metadata cataloging with Apache Atlas
- Data lineage tracking across transformations
- Consistency models in distributed AI storage
- Mounting cloud storage in Kubernetes pods
- Caching strategies for frequently accessed model data
- Data retention and lifecycle policies
- Backup and restore procedures for AI datasets
- Encryption of data at rest and in transit
- Access control for sensitive training data
- Data quality validation pipelines
- Anomaly detection in incoming AI data streams
Module 12: MLOps Frameworks and Tooling Ecosystems - Comparative analysis of Kubeflow, MLflow, and SageMaker
- Setting up a unified MLOps control plane
- Model versioning and experiment tracking
- Feature store implementation with Feast or Tecton
- Online vs offline feature serving patterns
- Feature consistency across training and inference
- Model monitoring for prediction skew and drift
- Automated retraining triggers based on data drift
- Model performance dashboards and alerts
- Model interpretability and explainability tools
- Fairness, bias, and ethical AI monitoring
- Regulatory compliance tooling for AI governance
- Integration with enterprise data warehouses
- Unified logging across training, testing, and deployment
- End-to-end automation from data to deployment
- Vendor-agnostic tool selection strategies
Module 13: Real-World Implementation Projects - Designing a cloud-native AI platform for financial fraud detection
- Implementing CI/CD for a computer vision pipeline
- Building a self-healing model deployment system
- Creating a multi-region inference cluster for global users
- Setting up automated compliance checks for healthcare AI
- Optimizing inference latency in a recommendation engine
- Developing a GPU-sharing policy for research teams
- Deploying a serverless LLM summarization service
- Constructing a real-time anomaly detection architecture
- Integrating audit trails into model decision pipelines
- Automating model certification workflows
- Implementing zero-downtime model updates
- Building a disaster recovery plan for AI infrastructure
- Creating observability dashboards for executive reporting
- Scaling a speech recognition service during peak load
- Hardening an AI API for public exposure
Module 14: Integration with Enterprise Systems and Governance - Aligning AI DevOps with ITIL change management
- Integrating with enterprise monitoring suites
- Active directory integration for team access
- Single sign-on for AI platform dashboards
- Change advisory board (CAB) workflows for AI deployments
- Release calendar coordination across teams
- Service catalog registration for AI capabilities
- Disaster recovery testing for AI systems
- Business continuity planning for model services
- Capacity reporting for cloud cost centers
- Budget forecasting for AI infrastructure
- Vendor contract management for cloud AI services
- Internal SLA definition for AI team deliverables
- Knowledge transfer documentation standards
- Onboarding checklists for new AI engineers
- Audit preparation for SOX, HIPAA, or GDPR compliance
Module 15: Certification Preparation & Next Career Steps - Review of key cloud-native DevOps concepts for AI
- Practice scenarios for real-world troubleshooting
- Common pitfalls in AI infrastructure design
- Architectural decision records for complex systems
- Documentation best practices for production AI
- Presenting technical designs to cross-functional teams
- Communicating risk and tradeoffs to leadership
- Resume optimization for cloud-native AI roles
- LinkedIn profile enhancement with certification
- Interview preparation for DevOps and MLOps roles
- Negotiating technical leadership opportunities
- Building a personal brand in AI infrastructure
- Contributing to open-source projects in MLOps
- Speaking at industry conferences and meetups
- Mentoring junior engineers in cloud-native patterns
- Transitioning to AI platform architecture or engineering management
- NVIDIA GPU provisioning in Kubernetes clusters
- Device plugins and extended resources in K8s
- Monitoring GPU memory and utilization metrics
- Scheduling AI jobs to GPU-enabled nodes
- Time-slicing GPUs for multiple model tasks
- Virtual GPU allocation strategies
- Multi-instance GPU (MIG) configuration
- Monitoring tensor core usage in inference
- Thermal and power constraints in dense AI clusters
- Driver management and version compatibility
- Firmware updates for AI accelerators
- Resource quotas for GPU workloads
- Cost allocation by accelerator usage
- Hybrid CPU-GPU workload balancing
- Fault tolerance in GPU node failures
- Benchmarking model performance by hardware type
Module 11: Data Pipelines and Storage for AI Systems - Designing robust data ingestion frameworks
- Streaming vs batch data processing for AI
- Data lakehouse architectures with Delta Lake
- Schema evolution management in AI datasets
- Data partitioning strategies for query performance
- Metadata cataloging with Apache Atlas
- Data lineage tracking across transformations
- Consistency models in distributed AI storage
- Mounting cloud storage in Kubernetes pods
- Caching strategies for frequently accessed model data
- Data retention and lifecycle policies
- Backup and restore procedures for AI datasets
- Encryption of data at rest and in transit
- Access control for sensitive training data
- Data quality validation pipelines
- Anomaly detection in incoming AI data streams
Module 12: MLOps Frameworks and Tooling Ecosystems - Comparative analysis of Kubeflow, MLflow, and SageMaker
- Setting up a unified MLOps control plane
- Model versioning and experiment tracking
- Feature store implementation with Feast or Tecton
- Online vs offline feature serving patterns
- Feature consistency across training and inference
- Model monitoring for prediction skew and drift
- Automated retraining triggers based on data drift
- Model performance dashboards and alerts
- Model interpretability and explainability tools
- Fairness, bias, and ethical AI monitoring
- Regulatory compliance tooling for AI governance
- Integration with enterprise data warehouses
- Unified logging across training, testing, and deployment
- End-to-end automation from data to deployment
- Vendor-agnostic tool selection strategies
Module 13: Real-World Implementation Projects - Designing a cloud-native AI platform for financial fraud detection
- Implementing CI/CD for a computer vision pipeline
- Building a self-healing model deployment system
- Creating a multi-region inference cluster for global users
- Setting up automated compliance checks for healthcare AI
- Optimizing inference latency in a recommendation engine
- Developing a GPU-sharing policy for research teams
- Deploying a serverless LLM summarization service
- Constructing a real-time anomaly detection architecture
- Integrating audit trails into model decision pipelines
- Automating model certification workflows
- Implementing zero-downtime model updates
- Building a disaster recovery plan for AI infrastructure
- Creating observability dashboards for executive reporting
- Scaling a speech recognition service during peak load
- Hardening an AI API for public exposure
Module 14: Integration with Enterprise Systems and Governance - Aligning AI DevOps with ITIL change management
- Integrating with enterprise monitoring suites
- Active directory integration for team access
- Single sign-on for AI platform dashboards
- Change advisory board (CAB) workflows for AI deployments
- Release calendar coordination across teams
- Service catalog registration for AI capabilities
- Disaster recovery testing for AI systems
- Business continuity planning for model services
- Capacity reporting for cloud cost centers
- Budget forecasting for AI infrastructure
- Vendor contract management for cloud AI services
- Internal SLA definition for AI team deliverables
- Knowledge transfer documentation standards
- Onboarding checklists for new AI engineers
- Audit preparation for SOX, HIPAA, or GDPR compliance
Module 15: Certification Preparation & Next Career Steps - Review of key cloud-native DevOps concepts for AI
- Practice scenarios for real-world troubleshooting
- Common pitfalls in AI infrastructure design
- Architectural decision records for complex systems
- Documentation best practices for production AI
- Presenting technical designs to cross-functional teams
- Communicating risk and tradeoffs to leadership
- Resume optimization for cloud-native AI roles
- LinkedIn profile enhancement with certification
- Interview preparation for DevOps and MLOps roles
- Negotiating technical leadership opportunities
- Building a personal brand in AI infrastructure
- Contributing to open-source projects in MLOps
- Speaking at industry conferences and meetups
- Mentoring junior engineers in cloud-native patterns
- Transitioning to AI platform architecture or engineering management
- Comparative analysis of Kubeflow, MLflow, and SageMaker
- Setting up a unified MLOps control plane
- Model versioning and experiment tracking
- Feature store implementation with Feast or Tecton
- Online vs offline feature serving patterns
- Feature consistency across training and inference
- Model monitoring for prediction skew and drift
- Automated retraining triggers based on data drift
- Model performance dashboards and alerts
- Model interpretability and explainability tools
- Fairness, bias, and ethical AI monitoring
- Regulatory compliance tooling for AI governance
- Integration with enterprise data warehouses
- Unified logging across training, testing, and deployment
- End-to-end automation from data to deployment
- Vendor-agnostic tool selection strategies
Module 13: Real-World Implementation Projects - Designing a cloud-native AI platform for financial fraud detection
- Implementing CI/CD for a computer vision pipeline
- Building a self-healing model deployment system
- Creating a multi-region inference cluster for global users
- Setting up automated compliance checks for healthcare AI
- Optimizing inference latency in a recommendation engine
- Developing a GPU-sharing policy for research teams
- Deploying a serverless LLM summarization service
- Constructing a real-time anomaly detection architecture
- Integrating audit trails into model decision pipelines
- Automating model certification workflows
- Implementing zero-downtime model updates
- Building a disaster recovery plan for AI infrastructure
- Creating observability dashboards for executive reporting
- Scaling a speech recognition service during peak load
- Hardening an AI API for public exposure
Module 14: Integration with Enterprise Systems and Governance - Aligning AI DevOps with ITIL change management
- Integrating with enterprise monitoring suites
- Active directory integration for team access
- Single sign-on for AI platform dashboards
- Change advisory board (CAB) workflows for AI deployments
- Release calendar coordination across teams
- Service catalog registration for AI capabilities
- Disaster recovery testing for AI systems
- Business continuity planning for model services
- Capacity reporting for cloud cost centers
- Budget forecasting for AI infrastructure
- Vendor contract management for cloud AI services
- Internal SLA definition for AI team deliverables
- Knowledge transfer documentation standards
- Onboarding checklists for new AI engineers
- Audit preparation for SOX, HIPAA, or GDPR compliance
Module 15: Certification Preparation & Next Career Steps - Review of key cloud-native DevOps concepts for AI
- Practice scenarios for real-world troubleshooting
- Common pitfalls in AI infrastructure design
- Architectural decision records for complex systems
- Documentation best practices for production AI
- Presenting technical designs to cross-functional teams
- Communicating risk and tradeoffs to leadership
- Resume optimization for cloud-native AI roles
- LinkedIn profile enhancement with certification
- Interview preparation for DevOps and MLOps roles
- Negotiating technical leadership opportunities
- Building a personal brand in AI infrastructure
- Contributing to open-source projects in MLOps
- Speaking at industry conferences and meetups
- Mentoring junior engineers in cloud-native patterns
- Transitioning to AI platform architecture or engineering management
- Aligning AI DevOps with ITIL change management
- Integrating with enterprise monitoring suites
- Active directory integration for team access
- Single sign-on for AI platform dashboards
- Change advisory board (CAB) workflows for AI deployments
- Release calendar coordination across teams
- Service catalog registration for AI capabilities
- Disaster recovery testing for AI systems
- Business continuity planning for model services
- Capacity reporting for cloud cost centers
- Budget forecasting for AI infrastructure
- Vendor contract management for cloud AI services
- Internal SLA definition for AI team deliverables
- Knowledge transfer documentation standards
- Onboarding checklists for new AI engineers
- Audit preparation for SOX, HIPAA, or GDPR compliance