Cloud Native Computing Foundation (CNCF) Guaranteed-to-Run

Cloud Native AI Intermediate

The Cloud Native AI course by Cloud Native Computing Foundation (CNCF) equips cloud engineers and AI platform engineers with skills to deploy and manage AI workloads on Kubernetes, solving the critical pain point of inconsistent AI infrastructure. With 58% of enterprises already using Kubernetes for AI, this training ensures portability and reliability across environments through standardized conformance practices.

Prepare for the CNCF Certified Kubernetes AI Conformance credential with Koenig’s official vendor-authorized courseware and 30-day lab access. Gain hands-on expertise in GPU integration, scheduling, and security to lead production-grade AI deployments on Kubernetes.

16 Hours (2 Days)
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0+ professionals trained

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Course Overview

The Cloud Native AI course by the Cloud Native Computing Foundation (CNCF) is designed to equip IT professionals with foundational knowledge in Kubernetes and the broader cloud native ecosystem, specifically aligned with the Kubernetes and Cloud Native Associate (KCNA) certification exam. This course serves roles such as Junior DevOps Engineer, Cloud Support Associate, and Platform Engineer, providing a critical entry point into cloud-native technologies. With 84% of enterprises globally adopting Kubernetes, demand for certified professionals is surging, making this credential a valuable asset for individuals entering the cloud-native workforce. The curriculum emphasizes core principles of container orchestration, cloud native architecture, and application delivery, ensuring learners are prepared for real-world implementation.

Students engage with key technologies including Kubernetes, Helm, Prometheus, Grafana, Argo CD, and nginx-ingress, all within a hands-on lab environment powered by kind (Kubernetes in Docker). Through practical exercises, learners build and configure a complete cloud native application stack, including Deployments, Services, ConfigMaps, Secrets, Ingress controllers, RBAC policies, and observability tools. A capstone project involves deploying a full application using GitOps methodology with Helm and Argo CD, enabling automated synchronization from a GitHub repository. These labs simulate real-world scenarios such as cluster monitoring with Prometheus and Grafana, secure namespace isolation, and end-to-end application delivery, ensuring proficiency in production-grade workflows.

Earning the Kubernetes and Cloud Native Associate (KCNA) certification validates foundational skills recognized across the industry, serving as a stepping stone to advanced credentials like CKA or CKAD. Certified professionals report earning between $95,000 and $130,000 annually in the United States, with strong growth potential in cloud-focused roles. Koenig Solutions enhances preparation with official courseware, 1-on-1 mentoring, and a Guaranteed-to-Run schedule, ensuring learners gain hands-on experience with real-world tools. Completing this Cloud Native AI training positions individuals to confidently enter high-demand roles in DevOps, platform engineering, and cloud operations, driving long-term career advancement in the rapidly expanding cloud native landscape.

What You'll Learn

✓Deploy Kubeflow on Kubernetes clusters to achieve 99.9 percent uptime for machine learning operations within the Cloud Native AI framework by CNCF.
✓Implement data preparation pipelines using CNCF tools to increase model training accuracy by at least 15 percent.
✓Train machine learning models using the Kubeflow Unified Training Operator to reduce total training time by 20 percent.
✓Orchestrate complex machine learning workflows with Kubeflow Pipelines to decrease manual deployment latency by 30 percent.
✓Optimize hyperparameters using Katib in Cloud Native AI by CNCF to improve model performance metrics by 10 percent.
✓Secure and manage AI models within Kubeflow to ensure 100 percent compliance with data privacy standards in Cloud Native AI by CNCF.

Skills You'll Gain

CNCF Kubernetes CNCF Cloud Native Container Orchestration CNCF Prometheus GPU Resource Management CNCF AI Conformance Distributed Training Model Serving KServe Deployment Kubeflow Pipelines CNCF Kubeflow Observability Integration Auto Scaling High Performance Networking Production AI Deployment Resource Allocation

Prerequisites

Recommended knowledge before taking this course
  • Familiarity with core cloud-native concepts like containerization, microservices, and immutable infrastructure is essential for mastering Cloud Native AI courses offered by the Cloud Native Computing Foundation (CNCF). A working knowledge of Kubernetes (K8s), including pods, nodes, clusters, and orchestration, enhances your ability to deploy AI solutions efficiently. Understanding AI/ML workflows and tools such as Kubeflow is crucial for implementing scalable AI models in cloud environments. Experience with container runtime and orchestration concepts in distributed systems helps optimize AI workloads. A basic grasp of GPU acceleration and drivers in cloud settings ensures smooth AI training and inference. Awareness of CNCF ecosystem projects like Prometheus, Fluentd, and Envoy enables you to monitor and manage AI applications effectively. These prerequisites prepare you to leverage CNCF’s Cloud Native AI initiatives, which are vital for deploying intelligent applications at scale. According to recent industry reports, 78% of AI deployments now rely on cloud-native architectures for scalability and resilience, making this knowledge essential for future-ready AI professionals.
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Certification Exam

Everything you need to know about the Cloud Native AI certification exam

Exam Details
Exam Name
Format
Multiple choice, labs & case studies
Questions
Duration
Passing Score
Validity
Retake Policy
N/A
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Cloud Native AI

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Course Curriculum

Structured learning with hands-on labs and real-world scenarios

1
Day 1– Mastering Cloud Native AI Foundations
Core Cloud Native architecture principles for AI Evolution of AI systems within the CNCF ecosystem Integrating Cloud Native and AI workflows using KubeFlow Defining Cloud Native AI (CNCF) standards and architectural patterns Strategic benefits of Cloud Native AI for scalable infrastructure AI-driven Cloud Native system optimization using Prometheus metrics Real-world Cloud Native AI applications and industry benchmarks Overcoming Cloud Native AI implementation hurdles for KCNA/KCSA certification alignment
2
Day 2– Optimizing Data for AI Workloads
Scalable data collection strategies using cloud-native storage Efficient data cleaning and preprocessing in distributed environments Advanced feature engineering techniques for large-scale datasets Managing massive AI data volumes with CNCF-compliant storage interfaces Reliable data synchronization methods for distributed training Enterprise data governance policies for AI compliance Storage requirements for AI infrastructure using Rook/Ceph Comprehensive data pipeline observability with OpenTelemetry
3
Day 3– Advanced Model Training and Optimization
Strategic model selection approaches for cloud-native environments Modern neural network architectures for distributed training Automated hyperparameter tuning methods using KubeFlow Katib Distributed model training patterns on Kubernetes Optimized GPU allocation strategies for high-performance workloads Memory efficiency optimization techniques for deep learning models Automated training pipeline workflows with Argo Workflows Precision model performance tracking against industry benchmarks
4
Day 4– Cloud Native Model Serving and Deployment
Containerization for AI models using OCI-compliant images Deploying models using KubeFlow and Argo Workflows Robust model registry implementation for version control CI/CD pipelines for ML models using Argo CD Inference Service (KServe) and Model Mesh for high-demand workloads Latency reduction optimization techniques for real-time inference Automated model version management and rollback strategies Data-driven A/B testing strategies for model deployment
5
Day 5– Observability and Security in CNAI
Continuous model performance monitoring with Prometheus and Grafana Proactive model drift detection in production Security protocols for AI workloads using Falco for runtime threat detection Granular access control policies with SPIFFE/SPIRE Resource isolation and management for multi-tenant AI clusters Cloud Native cost optimization strategies for GPU-intensive workloads Enhanced user experience considerations for AI-integrated services Future-proofing Cloud Native AI solutions aligned with CNCF certification tracks

What's Included in Your Training

Every enrollment comes packed with resources to maximise your learning and exam success

Career Outcomes

78%

of Cloud Native AI certified professionals report career advancement within 6 months

Salary Impact

+15%

Average salary increase reported after obtaining the Cloud Native AI certification

Typical Salary Range (Global)
Entry$90,000–$115,000
Mid$115,000–$145,000
Senior$145,000–$180,000

*Source: Glassdoor / LinkedIn 2025

Job Roles

6
  • Cloud Native AI Platform Engineer
  • DevOps Engineer with Platform Focus
  • Site Reliability Engineer for AI
  • Internal Developer Platform Engineer
  • Developer Productivity Engineer
  • Cloud Native Infrastructure Specialist

Companies Hiring

5,000+
Google Microsoft Amazon Red Hat Infosys Fidelity Investments NatWest Group Ericsson Harness Inc GitHub

and 5,000+ organizations worldwide seeking Cloud Native AI certified professionals

Real Transformations

Course Student Reviews

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    “Our whole DevOps team got AZ-400 certified through Koenig's corporate training. Smooth logistics and top-tier MCTs throughout.”

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    Carlos R.

    Engineering Manager

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  • ★★★★★

    “Passed AZ-104 on first attempt. The MCT knew the exact exam patterns and the labs were exactly what Microsoft tests. Worth every penny.”

    Rahul M.

    Rahul M.

    Azure Administrator

    AZ-104 Certified ✓ Verified
  • ★★★★★

    “I trained 15 of my team members for SC-200. Koenig's on-site delivery was seamless and all 15 passed within 3 months.”

    Sarah K.

    Sarah K.

    CISO, Financial Services

    Enterprise Client ✓ Verified
  • ★★★★★

    “The 1-on-1 format was a game changer. My trainer adjusted the pace to my schedule and I cleared PL-300 while working full-time.”

    Ahmed R.

    Ahmed R.

    Business Intelligence Lead

    PL-300 Certified ✓ Verified
  • ★★★★★

    “From AZ-900 to AZ-305 in 6 months. Koenig's structured roadmap and MCT mentoring made the expert level achievable.”

    Priya S.

    Priya S.

    Cloud Solutions Architect

    AZ-305 Expert ✓ Verified
  • ★★★★★

    “As an L&D head I've used 5 training vendors. Koenig's MCT quality, MOC materials, and ESI compliance is in a different league.”

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  • ★★★★★

    “SC-900 and SC-300 back to back — both cleared first try. The security curriculum at Koenig is incredibly thorough and up to date.”

    Aisha N.

    Aisha N.

    Security Analyst

    SC-300 Certified ✓ Verified
  • ★★★★★

    “AI-102 was daunting but the trainer broke it down perfectly. Real Azure OpenAI labs made the difference. Highly recommend.”

    David L.

    David L.

    AI Engineer

    AI-102 Certified ✓ Verified
  • ★★★★★

    “DP-600 Fabric certification done in 3 weeks of part-time study. The customised schedule around my timezone was a lifesaver.”

    Mei W.

    Mei W.

    Data Platform Engineer

    DP-600 Certified ✓ Verified
  • ★★★★★

    “Our whole DevOps team got AZ-400 certified through Koenig's corporate training. Smooth logistics and top-tier MCTs throughout.”

    Carlos R.

    Carlos R.

    Engineering Manager

    AZ-400 Team Training ✓ Verified
  • ★★★★★

    “Passed AZ-104 on first attempt. The MCT knew the exact exam patterns and the labs were exactly what Microsoft tests. Worth every penny.”

    Rahul M.

    Rahul M.

    Azure Administrator

    AZ-104 Certified ✓ Verified
  • ★★★★★

    “I trained 15 of my team members for SC-200. Koenig's on-site delivery was seamless and all 15 passed within 3 months.”

    Sarah K.

    Sarah K.

    CISO, Financial Services

    Enterprise Client ✓ Verified
  • ★★★★★

    “The 1-on-1 format was a game changer. My trainer adjusted the pace to my schedule and I cleared PL-300 while working full-time.”

    Ahmed R.

    Ahmed R.

    Business Intelligence Lead

    PL-300 Certified ✓ Verified
  • ★★★★★

    “Passed AZ-104 on first attempt. The MCT knew the exact exam patterns and the labs were exactly what Microsoft tests. Worth every penny.”

    Rahul M.

    Rahul M.

    Azure Administrator

    AZ-104 Certified ✓ Verified
  • ★★★★★

    “I trained 15 of my team members for SC-200. Koenig's on-site delivery was seamless and all 15 passed within 3 months.”

    Sarah K.

    Sarah K.

    CISO, Financial Services

    Enterprise Client ✓ Verified
  • ★★★★★

    “The 1-on-1 format was a game changer. My trainer adjusted the pace to my schedule and I cleared PL-300 while working full-time.”

    Ahmed R.

    Ahmed R.

    Business Intelligence Lead

    PL-300 Certified ✓ Verified
  • ★★★★★

    “From AZ-900 to AZ-305 in 6 months. Koenig's structured roadmap and MCT mentoring made the expert level achievable.”

    Priya S.

    Priya S.

    Cloud Solutions Architect

    AZ-305 Expert ✓ Verified
  • ★★★★★

    “As an L&D head I've used 5 training vendors. Koenig's MCT quality, MOC materials, and ESI compliance is in a different league.”

    James T.

    James T.

    Head of L&D, UK Enterprise

    100+ Learners Trained ✓ Verified
  • ★★★★★

    “SC-900 and SC-300 back to back — both cleared first try. The security curriculum at Koenig is incredibly thorough and up to date.”

    Aisha N.

    Aisha N.

    Security Analyst

    SC-300 Certified ✓ Verified
  • ★★★★★

    “From AZ-900 to AZ-305 in 6 months. Koenig's structured roadmap and MCT mentoring made the expert level achievable.”

    Priya S.

    Priya S.

    Cloud Solutions Architect

    AZ-305 Expert ✓ Verified
  • ★★★★★

    “As an L&D head I've used 5 training vendors. Koenig's MCT quality, MOC materials, and ESI compliance is in a different league.”

    James T.

    James T.

    Head of L&D, UK Enterprise

    100+ Learners Trained ✓ Verified
  • ★★★★★

    “SC-900 and SC-300 back to back — both cleared first try. The security curriculum at Koenig is incredibly thorough and up to date.”

    Aisha N.

    Aisha N.

    Security Analyst

    SC-300 Certified ✓ Verified
  • ★★★★★

    “AI-102 was daunting but the trainer broke it down perfectly. Real Azure OpenAI labs made the difference. Highly recommend.”

    David L.

    David L.

    AI Engineer

    AI-102 Certified ✓ Verified
  • ★★★★★

    “DP-600 Fabric certification done in 3 weeks of part-time study. The customised schedule around my timezone was a lifesaver.”

    Mei W.

    Mei W.

    Data Platform Engineer

    DP-600 Certified ✓ Verified
  • ★★★★★

    “Our whole DevOps team got AZ-400 certified through Koenig's corporate training. Smooth logistics and top-tier MCTs throughout.”

    Carlos R.

    Carlos R.

    Engineering Manager

    AZ-400 Team Training ✓ Verified
  • ★★★★★

    “AI-102 was daunting but the trainer broke it down perfectly. Real Azure OpenAI labs made the difference. Highly recommend.”

    David L.

    David L.

    AI Engineer

    AI-102 Certified ✓ Verified
  • ★★★★★

    “DP-600 Fabric certification done in 3 weeks of part-time study. The customised schedule around my timezone was a lifesaver.”

    Mei W.

    Mei W.

    Data Platform Engineer

    DP-600 Certified ✓ Verified
  • ★★★★★

    “Our whole DevOps team got AZ-400 certified through Koenig's corporate training. Smooth logistics and top-tier MCTs throughout.”

    Carlos R.

    Carlos R.

    Engineering Manager

    AZ-400 Team Training ✓ Verified

Frequently Asked Questions

Everything you need to know about the Cloud Native AI training course

Is the Cloud Native AI certification exam included in the Koenig training course fee?
The Cloud Native AI certification exam is not included in the course tuition and requires a separate $250 purchase. This fee provides 12 months of scheduling eligibility and includes one free retake. Candidates must register directly via the Linux Foundation or authorized CNCF partners to sit for the KCNA or related Cloud Native Computing Foundation certification exams.
What training formats does Koenig offer for Cloud Native AI and is there a Guaranteed-to-Run schedule?
Koenig provides live, 1-on-1 instructor-led training for Cloud Native AI featuring a Guaranteed-to-Run schedule, ensuring your session proceeds regardless of enrollment numbers. While we do not offer self-paced e-learning, students gain access to comprehensive supplementary materials and live cloud labs. These resources ensure hands-on mastery of Cloud Native Computing Foundation standards during your intensive training period.
How long is lab access provided and what environment is used for Cloud Native AI training?
Koenig grants 30 days of dedicated lab access for Cloud Native AI, utilizing a secure, cloud-based sandbox environment. This platform features real Kubernetes clusters and official Cloud Native Computing Foundation tools. Students practice container orchestration, security, and cloud-native application deployment in a production-like environment, ensuring they gain the practical skills necessary for real-world enterprise cloud deployments.
What is the Koenig rescheduling and cancellation policy for Cloud Native AI training courses?
Koenig offers flexible rescheduling for Cloud Native AI training with at least 7 days' notice. Cancellations made 7 or more days prior to the start date incur no fees. Cancellations within 7 days result in a 15% administrative charge. We provide emergency rescheduling options with documentation to ensure maximum flexibility for both corporate teams and individual learners.
What is the exam format, question count, time limit, and passing score for Cloud Native AI certification?
The KCNA exam, vital for Cloud Native AI professionals, is a 90-minute, online proctored test featuring 40-60 multiple-choice questions. It evaluates core competencies in Kubernetes fundamentals, container orchestration, and cloud-native architecture. While the Cloud Native Computing Foundation does not disclose a specific passing score, the exam validates foundational conceptual understanding required for modern cloud-native roles.
How long is the Cloud Native AI certification valid and what is the renewal process?
The Cloud Native AI-related KCNA certification remains valid for 2 years. You can renew by retaking the exam for $250. Alternatively, the CARE Program allows for automatic renewal without extra fees if you earn or recertify a higher-level credential, such as CKA or CKS, after January 1, 2026. This keeps your Cloud Native Computing Foundation status current.
Does Koenig provide post-training support for Cloud Native AI, such as mentor access or retakes?
Koenig offers 90 days of post-training email support, access to recorded sessions, and expert mentorship for Cloud Native AI learners. While exam retakes are not included, students receive targeted exam preparation guidance and can re-enroll in future batches at a discounted rate. We provide the study materials and practice resources necessary for your continued professional growth.
What are the prerequisites or experience needed for the Cloud Native AI course and certification?
There are no formal prerequisites for the Cloud Native AI course or the KCNA certification. This program is designed for beginners with foundational IT knowledge. We recommend basic familiarity with Linux, containers, and networking. The certification validates entry-level understanding of Kubernetes, making it ideal for developers and IT professionals transitioning into high-demand cloud roles.
What is the career impact and average salary for professionals with Cloud Native AI certification?
Professionals with Cloud Native AI and KCNA certification experience a 20-30% boost in job visibility, with average salaries between $95,000 and $130,000 annually. Certified experts qualify for Cloud Engineer and DevOps roles. With 82% of enterprises now adopting Kubernetes for AI workloads, your Cloud Native Computing Foundation credential signals high-value expertise to top-tier employers.
How does instructor-led Cloud Native AI training compare to self-study for certification preparation?
Instructor-led Cloud Native AI training improves exam success rates by up to 40% compared to self-study. Koenig’s program combines structured learning, live labs, and expert mentorship to accelerate your mastery. While self-study relies on free Cloud Native Computing Foundation materials, our training reduces your time to certification and provides the hands-on experience required for professional success.

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