Run GPU and ML tasks efficiently in Kubernetes. Learn AI-driven scheduling for certified-level workloads
π₯ 443 students
π October 2025 update
Add-On Information:
Get Instant Notification of New Courses on our Telegram channel.
Noteβ Make sure your ππππ¦π² cart has only this course you're going to enroll it now, Remove all other courses from the ππππ¦π² cart before Enrolling!
- Course Overview
- This comprehensive course is meticulously designed for professionals aiming to master the deployment, orchestration, and optimization of cutting-edge Artificial Intelligence (AI) and Machine Learning (ML) workloads on Kubernetes, specifically leveraging Graphics Processing Units (GPUs). It delves deep into the intricate challenges and sophisticated solutions for running demanding computational tasks in a cloud-native environment, ensuring efficiency and scalability are paramount for modern AI initiatives.
- Through a rigorous curriculum, learners will navigate the complexities of integrating high-performance computing (HPC) with container orchestration, focusing on the unique demands of AI models, from training to inference. The course emphasizes practical application and theoretical understanding of how Kubernetes can serve as the backbone for next-generation AI infrastructure, providing a robust, fault-tolerant, and agile platform capable of handling diverse machine learning requirements.
- A core differentiator of this course is its dedication to certification-level preparation, featuring an expansive bank of 1500 certified questions. This intensive question-and-answer format is specifically tailored to reinforce knowledge, identify gaps, and build confidence for those pursuing advanced Kubernetes certifications with an AI/ML and GPU specialization, making it an invaluable resource for exam success and validating deep expertise in the field.
- Explore advanced topics such as AI-driven scheduling strategies, where Kubernetes intelligently allocates GPU and CPU resources to maximize throughput and minimize latency for diverse AI tasks. You will gain insight into how to configure and fine-tune your clusters for optimal performance, understanding the nuances of resource isolation, priority, preemption, and cost-efficiency in AI-centric environments. The course prepares you to architect and manage highly performant and resilient AI workload clusters.
- Requirements / Prerequisites
- A solid foundational understanding of Kubernetes core concepts, including Pods, Deployments, Services, Namespaces, and basic kubectl operations. Familiarity with Kubernetes architecture and its primary components is essential for grasping advanced topics presented in this specialized curriculum.
- Proficiency with the Linux command-line interface and basic system administration tasks, as Kubernetes environments often rely heavily on underlying Linux systems for cluster management, debugging, and advanced configurations.
- Working knowledge of containerization technologies, particularly Docker, including how to build, tag, and push container images to registries. Understanding container lifecycles, image layers, and best practices for containerizing applications is crucial.
- Basic familiarity with machine learning concepts and workflows, such as understanding the difference between training and inference, common ML frameworks (e.g., TensorFlow, PyTorch), and data handling strategies for ML models.
- While not strictly mandatory, a foundational understanding of Python programming and cloud computing concepts (e.g., AWS, GCP, Azure basics) will significantly enhance the learning experience and practical application exercises, especially during hands-on lab sections.
- Skills Covered / Tools Used
- GPU Orchestration and Management: Master the deployment and configuration of NVIDIA GPU Operator, understanding device plugins, and effectively allocating GPU resources to containers within Kubernetes pods. Learn to troubleshoot common GPU-related issues and manage shared GPU access.
- Advanced Kubernetes Scheduling for AI: Implement and optimize custom schedulers, priority and preemption, gang scheduling, and topology management for complex distributed ML workloads. Understand how to leverage scheduler extensions and policies to achieve optimal resource utilization and task completion for GPU-intensive applications.
- MLOps Tooling and Frameworks on K8s: Gain hands-on experience with popular MLOps tools integrated with Kubernetes, including deploying and managing Kubeflow components, utilizing Argo Workflows for ML pipelines, and custom operators designed for AI/ML lifecycle management.
- Persistent Storage for ML Data: Configure and manage various Persistent Volumes (PVs) and Persistent Volume Claims (PVCs) for large ML datasets, exploring CSI drivers, shared file systems (e.g., NFS, CephFS), and object storage integration for Kubernetes-native AI applications, ensuring data locality and high throughput.
- Monitoring and Logging for AI/GPU Clusters: Deploy and configure robust monitoring solutions like Prometheus and Grafana for tracking GPU utilization, pod performance, and ML job progress. Implement centralized logging with tools like Fluentd and Elasticsearch for effective debugging and analysis of AI workloads.
- Networking for Distributed AI: Understand and implement advanced networking configurations for high-throughput distributed ML training across multiple GPUs and nodes, including RDMA over Converged Ethernet (RoCE) and high-speed interconnects to minimize communication overhead.
- Security Best Practices for AI Workloads: Learn to secure Kubernetes clusters hosting sensitive AI models and data, covering network policies, Pod Security Standards, secrets management, image scanning for vulnerabilities, and role-based access control (RBAC) for AI development teams.
- Performance Tuning and Troubleshooting: Develop expertise in diagnosing and resolving performance bottlenecks unique to GPU-accelerated AI workloads on Kubernetes, optimizing container configurations, and fine-tuning cluster settings for maximum efficiency and reduced training times.
- Certification Exam Strategy and Practice: Utilize the 1500 certified questions to solidify understanding across all modules, simulate exam conditions, and develop effective strategies for tackling complex, scenario-based questions in Kubernetes certification exams focused on advanced workloads, ensuring readiness and confidence.
- Benefits / Outcomes
- You will become proficient in designing, deploying, and managing robust, scalable, and efficient AI/ML platforms on Kubernetes, making you an indispensable asset in organizations adopting cloud-native AI strategies and seeking to industrialize their machine learning operations.
- Gain the critical expertise to optimize GPU utilization and ML task scheduling, significantly reducing infrastructure costs, accelerating model development cycles, and improving resource efficiency, directly impacting your organization’s bottom line and innovation pace.
- Achieve a deep, practical understanding that prepares you comprehensively for advanced Kubernetes certifications, particularly those with a focus on AI, ML, and GPU orchestration, significantly boosting your professional credentials and opening doors to specialized roles.
- Develop the ability to troubleshoot complex performance and operational issues inherent to GPU-accelerated AI workloads, enabling you to maintain highly available and performant ML environments that meet demanding production requirements.
- Position yourself as a leading expert capable of bridging the gap between data science and DevOps, driving the adoption of best practices for MLOps in Kubernetes environments and contributing to more reliable, repeatable, and scalable AI deployments.
- PROS
- Unparalleled Certification Preparation: The inclusion of 1500 certified questions provides an extremely thorough and practical approach to preparing for advanced Kubernetes certification exams, focusing directly on the nuances of AI/ML and GPU workloads, greatly enhancing exam readiness.
- Highly Specialized and In-Demand Skill Set: This course targets a niche yet rapidly expanding area at the intersection of Kubernetes, AI, ML, and GPU computing, equipping learners with skills that are critically sought after by leading tech companies and research institutions.
- Focus on Efficiency and Optimization: Emphasizes practical strategies and AI-driven scheduling techniques to run GPU and ML tasks with maximum efficiency, leading to significant cost savings and performance improvements in real-world scenarios, making your deployments cost-effective.
- Cutting-Edge Content: The curriculum is regularly updated (as indicated by “October 2025 update”), ensuring the content remains relevant with the latest advancements in Kubernetes, AI frameworks, and GPU technologies, keeping learners ahead of the curve in a rapidly evolving domain.
- CONS
- The advanced nature and depth of this course necessitate a strong foundational understanding of Kubernetes and basic ML concepts, which might pose a steep learning curve for absolute beginners in either domain, potentially requiring prerequisite study.
Learning Tracks: English,IT & Software,IT Certifications
Found It Free? Share It Fast!