Containers and Virtual Machines (VMs) Training in Taichung

Edge AI represents a paradigm that focuses on executing machine learning inference close to the source of data to achieve low-latency, efficient, and scalable processing.

This instructor-led live training (available online or onsite) is designed for intermediate to advanced practitioners who wish to deploy, orchestrate, and optimize AI workloads within Kubernetes-based edge environments.

Upon completing this course, participants will be able to:

• Establish lightweight Kubernetes distributions suitable for edge deployments.
• Effectively deploy AI inference workloads across resource-constrained edge nodes.
• Address connectivity challenges and implement synchronization patterns.
• Optimize performance, storage, and networking for real-world edge scenarios.

Course Format

• Guided presentations supplemented by real-world examples.
• Scenario-based labs and practical exercises focused on edge deployment.
• Hands-on experience working with Kubernetes edge frameworks.

Customization Options

• To request a customized training session tailored to your specific edge platform requirements, please contact us to arrange it.

Kubernetes serves as a widely adopted container orchestration platform for managing distributed applications at scale.

This instructor-led live training (available online or onsite) targets advanced practitioners seeking to leverage AI and machine learning techniques to optimize Kubernetes resource usage, scheduling decisions, and autoscaling strategies.

Upon completing this program, participants will be able to:

• Apply AI/ML models to enhance workload scheduling decisions within Kubernetes.
• Utilize predictive analytics to optimize CPU, GPU, and memory allocation.
• Implement intelligent autoscaling through reinforcement learning and metric forecasting.
• Reduce infrastructure costs and latency via automated resource optimization.

Course Format

• Instructor-guided technical presentations and in-depth discussions.
• Hands-on lab exercises using real Kubernetes clusters.
• Practical exercises applying AI models to real-world operational scenarios.

Course Customization Options

• To tailor this course to your specific platform setup or operational needs, please contact us for customization.

MLOps on Kubernetes provides a framework for automating the training, validation, packaging, and deployment of machine learning models through containerized pipelines and GitOps workflows.

This instructor-led live training (available online or onsite) is designed for intermediate-level practitioners looking to build automated, scalable MLOps pipelines on Kubernetes.

Upon completing this training, participants will be able to:

• Design end-to-end CI/CD pipelines for machine learning.
• Implement GitOps workflows for model deployment and versioning.
• Automate the training, testing, and packaging of ML models.
• Integrate monitoring, alerting, and rollback strategies.

Course Format

• Instructor-guided presentations and technical deep dives.
• Hands-on exercises focused on building real-world CI/CD workflows.
• Live-lab practice deploying ML workloads to Kubernetes.

Course Customization Options

• Organizations may request tailored content aligned with their internal MLOps tools and infrastructure.

Kubeflow is an open-source platform designed to streamline building, training, and deploying machine learning workloads on Kubernetes.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level professionals who wish to build reliable ML workflows using Kubeflow.

Upon completion of this training, attendees will gain the skills to:

• Navigate the Kubeflow ecosystem and core components.
• Build reproducible workflows with Kubeflow Pipelines.
• Run scalable training jobs on Kubernetes.
• Serve machine learning models efficiently using Kubeflow Serving.

Format of the Course

• Guided presentations and collaborative discussions.
• Hands-on labs with real Kubeflow components.
• Practical exercises to build end-to-end ML workflows.

Course Customization Options

• Customized versions of this training can be arranged to align with your team’s technology stack and project requirements.

CI/CD for AI represents a systematic methodology for automating the packaging, testing, containerization, and deployment of machine learning models through continuous integration and continuous delivery pipelines.

This instructor-led live training, available both online and onsite, targets intermediate-level professionals aiming to automate end-to-end AI model delivery workflows using Docker and CI/CD platforms.

Upon completing the training, participants will be able to:

• Establish automated pipelines for building and testing AI model containers.
• Implement version control and ensure reproducibility throughout model lifecycles.
• Integrate automated deployment strategies for AI services.
• Apply CI/CD best practices specifically tailored for machine learning operations.

Course Format

• Instructor-guided presentations and technical discussions.
• Practical labs and hands-on implementation exercises.
• Realistic CI/CD workflow simulations in a controlled environment.

Course Customization Options

• For organizations requiring customized pipeline workflows or specific platform integrations, please contact us to tailor this course to your needs.

This instructor-led, live training in Taichung (online or onsite) is aimed at advanced-level Kubernetes administrators and DevOps engineers who wish to enhance their monitoring skills for Kubernetes clusters using Prometheus and Grafana.

By the end of this training, participants will be able to:

• Set up Prometheus and Grafana for Kubernetes monitoring.
• Monitor key metrics for pods, nodes, and services.
• Create dynamic dashboards to visualize cluster health and performance.
• Implement alerting strategies for proactive issue resolution.
• Apply best practices for scaling monitoring solutions in Kubernetes environments.

Hybrid AI deployment involves executing AI inference across cloud, on-premise, and edge environments through unified container-based workflows.

This instructor-led live training, available online or onsite, is designed for advanced professionals seeking to design and deploy distributed AI inference systems across heterogeneous environments.

Upon completing this training, participants will be able to:

• Construct secure and scalable containerized AI services for multi-location settings.
• Deploy AI inference workloads to cloud platforms, local servers, and edge devices using Docker.
• Integrate orchestration tools to automate distributed AI operations.
• Optimize inference latency, reliability, and resilience across diverse infrastructure.

Course Format

• Guided presentations and expert-led discussions.
• Extensive hands-on practice and applied exercises.
• Real-world experimentation in a controlled live-lab environment.

Customization Options

• To tailor this course to your organization’s infrastructure or specific use cases, please contact us for customization services.

Kubernetes is an open-source platform designed to automate the deployment, scaling, and management of containerized applications.

This instructor-led, live training (available online or onsite) is designed for beginner to intermediate IT professionals who want to learn the core concepts and components of Kubernetes and apply them to manage containerized applications at scale.

By the end of this training, participants will be able to:

• Understand Kubernetes architecture and its components.
• Deploy and manage containers within a Kubernetes cluster.
• Configure networking, storage, and scaling for workloads.
• Troubleshoot common issues and follow best practices for cluster operations.

Format of the Course

• Interactive lecture and discussion.
• Abundant exercises and practice sessions.
• Hands-on implementation in a live lab environment.

Course Customization Options

• To request customized training for this course, please contact us to arrange.

This instructor-led, live training in Taichung (online or onsite) is designed for DevOps engineers and developers who want to leverage Kubernetes to build, deploy, and manage containers and cluster components in a secure and scalable environment.

By the end of this training, participants will be able to:

• Understand the architecture, core concepts, and components of the Kubernetes ecosystem.
• Set up, install, and configure a Kubernetes cluster for container orchestration.
• Learn how to execute Kubernetes operations using command-line tools.
• Gain hands-on experience ranging from basic to advanced Kubernetes operations and administration.

Docker serves as a containerization platform designed to establish portable, isolated, and secure deployment environments for AI inference services.

This instructor-led live training, available online or onsite, targets beginner to intermediate technical professionals seeking to develop secure, portable AI inference microservices that deploy consistently across local machines, servers, and cloud VMs.

Upon completing this workshop, participants will be capable of:

• Constructing lightweight inference containers suitable for both local and cloud deployment.
• Securing containerized AI services through best-practice techniques.
• Establishing portable microservice workflows to ensure consistent environments.
• Deploying AI inference endpoints across diverse infrastructure setups.

Course Format

• Guided lectures combined with practical demonstrations.
• Hands-on exercises to reinforce deployment and security methodologies.
• Live-lab practice focused on building and running portable inference services.

Customization Options

• To customize this training for your specific infrastructure or AI tooling stack, please contact us to arrange.

In this instructor-led live training at Taichung (onsite or remote), participants will learn how to deploy sample servers within containers and subsequently automate, scale, and manage these containerized servers inside a Kubernetes cluster. The training advances to more complex subjects, walking participants through the steps to secure, network, and monitor a Kubernetes cluster.

By the end of this training, participants will be able to:

• Set up and run a Docker container.
• Deploy containerized databases and servers.
• Set up a Docker and Kubernetes cluster.
• Use Kubernetes to deploy and manage different environments under the same cluster.
• Secure, scale and monitor a Kubernetes cluster.

GPU acceleration is critical for executing high-performance deep learning workloads in a scalable and efficient manner.

This instructor-led, live training (available online or onsite) is designed for intermediate-level technical professionals who want to configure, optimize, and deploy GPU-enabled AI workloads within Docker containers.

Upon completion of this course, participants will be able to:

• Build and run GPU-enabled containers for training and inference.
• Configure CUDA, drivers, and runtime libraries for containerized AI workflows.
• Optimize resource allocation and isolation for GPU-intensive applications.
• Deploy scalable, containerized deep learning services in production environments.

Format of the Course

• Interactive instruction supported by real-world demonstrations.
• Exercise-driven practice focused on GPU-enabled development.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• For tailored training aligned with your infrastructure or GPU stack, please contact us to arrange.

This instructor-led, live training in Taichung (online or onsite) is aimed at intermediate-level professionals who wish to effectively deploy, manage, and scale containerized applications using Kubernetes.

By the end of this training, participants will be able to:

• Understand the Kubernetes architecture and its components.
• Isolate resources effectively using Namespaces.
• Manage and customize workloads with Deployments, StatefulSets, and DaemonSets.
• Define computational resources using Requests and Limits.
• Work with Jobs and CronJobs for scheduled tasks.
• Understand Services and DNS within Kubernetes.
• Expose applications using Ingress.
• Manage ConfigMaps, Secrets, and Persistent Volumes.
• Scale and upgrade Kubernetes clusters using advanced strategies.
• Analyze and troubleshoot Kubernetes issues.
• Deploy resources efficiently using Helm Charts.

This instructor-led, live training in Taichung (online or onsite) is designed for intermediate to advanced developers, DevOps professionals, and architects who wish to design, deploy, and manage resilient applications using microservices, containers, and continuous integration/continuous deployment (CI/CD) pipelines.

By the end of this training, participants will be able to:

• Understand and implement microservices architecture.
• Deploy and manage containerized applications with Docker and Kubernetes.
• Set up and optimize CI/CD pipelines for automated deployments.
• Apply best practices for security, monitoring, and observability.

This instructor-led, live training in Taichung (online or onsite) is aimed at advanced-level platform engineers and DevOps professionals who wish to master scaling applications using microservices and Kubernetes.

By the end of this training, participants will be able to:

• Design and implement scalable microservices architectures.
• Deploy and manage applications on Kubernetes clusters.
• Utilize Helm charts for efficient service deployment.
• Monitor and maintain the health of microservices in production.
• Apply best practices for security and compliance in a Kubernetes environment.

Learn the fundamentals of containers, Kubernetes and OpenShift in a practical, hands-on training designed for developers, DevOps engineers and IT professionals. Participants will learn how to build containerized applications, deploy workloads, manage Kubernetes resources and use OpenShift to streamline modern application delivery in cloud and hybrid environments.

Docker serves as a containerization platform designed to build reproducible, portable, and scalable environments for machine learning systems.

This instructor-led, live training (available online or onsite) targets intermediate to advanced technical professionals who aim to containerize and operationalize complete ML pipelines using Docker.

Upon completion of this training, participants will be able to:

• Containerize ML training, validation, and inference workloads.
• Design and orchestrate end-to-end ML pipelines using Docker and supporting tools.
• Implement versioning, reproducibility, and CI/CD for ML components.
• Deploy, monitor, and scale ML services in containerized environments.

Course Format

• Interactive lectures supplemented by practical demonstrations.
• Hands-on exercises focused on building real ML pipeline components.
• Live-lab implementation for end-to-end containerized workflows.

Course Customization Options

• For customized training aligned with specific ML infrastructure needs, please contact us to discuss options.

Docker serves as a containerization platform designed to establish consistent, portable, and reproducible environments for artificial intelligence and machine learning applications.

This instructor-led live training, available online or onsite, targets intermediate professionals seeking to package ML codebases, dependencies, and models using Docker to ensure reliable workflows from development to production.

Upon completing this course, participants will be able to:

• Create and manage Docker images specifically designed for AI and ML applications.
• Containerize machine learning pipelines, tools, and their dependencies.
• Optimize Docker environments for enhanced performance and portability.
• Deploy containerized ML services across various runtime environments.

Course Format

• Concept demonstrations supported by guided discussion.
• Hands-on exercises focused on real-world containerization tasks.
• Practical implementation using live-lab Docker environments.

Customization Options

• To tailor this training to your organization's specific needs, please contact us to make arrangements.

This instructor-led live training, held in Taichung (online or onsite), is designed for beginner-level developers who wish to grasp the essentials of Kubefirst and its role in simplifying, securing, and accelerating Kubernetes and Swarm cluster management at enterprise scale.

By the end of this training, participants will be able to:

• Set up a Kubefirst development environment.
• Write and run a basic Kubefirst program.
• Annotate code with Kubefirst directives and clauses.
• Use Kubefirst API and libraries.
• Profile and debug Kubefirst programs.

This instructor-led, live training in Taichung (online or onsite) is aimed at intermediate-level developers and DevOps engineers who wish to use Minikube as a part of their development workflow.

By the end of this training, participants will be able to:

• Set up and manage a local Kubernetes environment using Minikube.
• Understand how to deploy, manage, and debug applications on Minikube.
• Integrate Minikube into their continuous integration and deployment pipelines.
• Optimize their development process using Minikube's advanced features.
• Apply best practices for local Kubernetes development.

This instructor-led live training in Taichung (online or onsite) targets intermediate-level developers and DevOps engineers aiming to build, deploy, and manage microservices using Spring Cloud and Docker.

By the conclusion of this training, participants will be able to:

• Develop microservices utilizing Spring Boot and Spring Cloud.
• Containerize applications using Docker and Docker Compose.
• Implement service discovery, API gateways, and inter-service communication.
• Monitor and secure microservices within production environments.
• Deploy and orchestrate microservices using Kubernetes.

This instructor-led, live training in Taichung (online or onsite) is designed for beginner to intermediate software developers and DevOps professionals who want to learn how to set up and manage a local Kubernetes environment using Minikube.

By the end of this training, participants will be able to:

• Install and configure Minikube on their local machine.
• Understand the basic concepts and architecture of Kubernetes.
• Deploy and manage containers using kubectl and the Minikube dashboard.
• Set up persistent storage and networking solutions for Kubernetes.
• Utilize Minikube for developing, testing, and debugging applications.

Course Objectives: Acquire theoretical and operational skills in Docker and Kubernetes.

Through this instructor-led, live training in Taichung (onsite or remote), participants will learn to create and manage Docker containers and deploy a sample application within one. Attendees will also gain skills in automating, scaling, and managing containerized applications using a Kubernetes cluster. The training concludes with advanced topics, guiding participants through the processes of securing, scaling, and monitoring a Kubernetes cluster.

Upon completing this training, participants will be able to:

• Set up and operate a Docker container.
• Deploy a containerized server and web application.
• Build and manage Docker images.
• Configure a Docker and Kubernetes cluster.
• Utilize Kubernetes to deploy and manage a clustered web application.
• Secure, scale, and monitor a Kubernetes cluster.

The Certified Kubernetes Administrator (CKA) program was established by The Linux Foundation and the Cloud Native Computing Foundation (CNCF).

Kubernetes has emerged as a leading platform for container orchestration.

NobleProg has been delivering Docker & Kubernetes training since 2015. With over 360 successfully completed training projects, we have become one of the most renowned training providers globally in the field of containerization.

Since 2019, we have also been assisting our customers in validating their performance in k8s environments by preparing them for and encouraging them to pass the CKA and CKAD exams.

This instructor-led, live training (available online or onsite) is designed for System Administrators and Kubernetes users who wish to validate their knowledge by passing the CKA exam.

Additionally, the training focuses on gaining practical experience in Kubernetes Administration, so we recommend participating even if you do not intend to take the CKA exam.

Course Format

• Interactive lecture and discussion.
• Extensive exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request customized training for this course, please contact us to arrange.
• To learn more about CKA certification, please visit: https://training.linuxfoundation.org/certification/certified-kubernetes-administrator-cka

In this instructor-led, live training in Taichung (online or onsite), participants will learn how to set up and manage a production-grade container environment using Kubernetes on AKS.

By the end of this training, participants will be able to:

• Configure and manage Kubernetes on AKS.
• Deploy, manage, and scale a Kubernetes cluster.
• Deploy containerized (Docker) applications on Azure.
• Migrate an existing Kubernetes environment from on-premise to the AKS cloud.
• Integrate Kubernetes with third-party continuous integration (CI) software.
• Ensure high availability and disaster recovery in Kubernetes.

Learn how to build, deploy, and administer containerized environments using Docker, Kubernetes, and OpenShift. This five-day, hands-on training covers container images, Kubernetes workloads, cluster networking, storage, security, monitoring, and practical OpenShift administration. Participants gain the skills needed to operate modern container platforms and troubleshoot applications across development and production environments.

This instructor-led live training, available both online and on-site, is designed for engineers who want to automate, secure, and monitor containerized applications within large-scale Kubernetes clusters.

Upon completion of this training, participants will be able to:

• Utilize Kubernetes to deploy and manage multiple environments within a single cluster
• Secure, scale, and monitor a Kubernetes cluster

Course Format

• Interactive lectures and discussions
• Extensive exercises and practice sessions
• Hands-on implementation in a live-lab environment

Customization Options

• For information on arranging customized training for this course, please contact us.

The Certified Kubernetes Application Developer (CKAD) credential was established by The Linux Foundation and the Cloud Native Computing Foundation (CNCF), the governing body for Kubernetes.

This instructor-led live training (available online or onsite) is designed for developers who want to validate their ability to design, build, configure, and expose cloud-native applications on Kubernetes.

Additionally, the training emphasizes practical experience in Kubernetes application development, so we highly recommend attending even if you do not plan to take the CKAD exam.

NobleProg has been providing Docker and Kubernetes training since 2015. With over 360 successfully completed training projects, we have become one of the most recognized training providers globally in the field of containerization. Since 2019, we have also helped our customers validate their performance in Kubernetes environments by preparing and encouraging them to pass the CKA and CKAD exams.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practice.
• Hands-on implementation in a live laboratory environment.

Course Customization Options

• To request customized training for this course, please contact us to arrange.
• To learn more about CKAD, please visit: https://training.linuxfoundation.org/certification/certified-kubernetes-application-developer-ckad/

This instructor-led, live course in Taichung provides participants with an overview of Rancher and demonstrates through hands-on practice how to deploy and manage a Kubernetes cluster with Rancher.

Istio is an open-source service mesh that operates on Kubernetes, facilitating secure, observable, and manageable connectivity among microservices. By utilizing Istio's Envoy-based sidecar proxies, teams can enforce policies, secure communications through mTLS, gain deep insights into traffic patterns, and enhance reliability at scale.

This instructor-led, live training (available online or onsite) is designed for intermediate-level engineers who aim to deploy, secure, and manage microservices applications using Istio on Kubernetes.

Upon completing this training, participants will be capable of:

• Installing and configuring Istio on Kubernetes clusters.
• Understanding and applying service mesh concepts, including traffic management, security, and observability.
• Deploying microservices applications within an Istio service mesh.
• Securing service-to-service communications with mutual TLS (mTLS) and Zero Trust principles.
• Monitoring, tracing, and troubleshooting microservices using Prometheus, Grafana, and Jaeger.
• Integrating Istio with Calico for advanced network policies and security.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practice sessions.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

The rapid advancement of microservices and container technologies in recent years has fundamentally transformed how we design, develop, deploy, and operate software. Today's applications must be optimized for scalability, elasticity, resilience, and adaptability. To meet these evolving demands, modern architectures necessitate a distinct set of patterns and practices. This training explores strategies to identify, comprehend, and adapt to these new requirements.

Target Audience
This course is designed for individuals who possess a foundational understanding of container technology and Kubernetes concepts but may lack extensive practical experience. Drawing on use cases and lessons learned from real-world projects, the training aims to inspire participants to create and manage superior cloud-native applications.

• Developers
• Operations Staff
• DevOps Engineers
• QA Engineers
• IT Project Managers

Course Format

• Interactive lectures and discussions
• Extensive exercises and hands-on practice
• Practical implementation in a live laboratory environment

Course Customization Options

• For inquiries regarding customized training for this course, please contact us to make arrangements.

This instructor-led, live training in Taichung (online or onsite) is designed for engineers who want to leverage Helm to simplify the installation and management of Kubernetes applications.

Upon completion of this training, participants will be able to:

• Install and configure Helm.
• Create reproducible builds of Kubernetes applications.
• Distribute applications as Helm charts.
• Run third-party applications packaged as Helm charts.
• Manage releases of Helm packages.

This instructor-led, live training in Taichung (online or onsite) is designed for DevOps engineers who aim to automate the DevOps lifecycle using Kubernetes and GitLab.

By the end of this training, participants will be able to:

• Automate application builds, tests, and deployments.
• Create an automated build infrastructure.
• Deploy an application to a containerized cloud environment.

This instructor-led, live training in Taichung (online or onsite) is intended for Kubernetes professionals who wish to prepare for the CKS exam.

By the end of this training, participants will understand how to secure Kubernetes environments and container-based applications throughout the various stages of an application's lifecycle: build, deployment, and runtime.

This instructor-led live training (online or onsite) is aimed at developers and DevOps engineers who wish to utilize a serverless approach for building enterprise applications in Kubernetes.

By the end of this training, participants will be able to:

• Set up and configure the Kubernetes system to start developing with a serverless architecture.
• Understand the concepts and principles foundational to serverless environments.
• Operate toolchains necessary for serverless development and integrate them with Kubernetes components.
• Apply their Python programming skills to implement serverless systems.
• Secure enterprise applications that are deployed through a serverless framework on Kubernetes.
• Utilize modern cloud computing methods in optimizing DevOps task processing workflows.

This instructor-led, live training in Taichung (online or onsite) is designed for engineers who want to enhance the security of their Kubernetes clusters beyond the default settings.

Upon completing this training, participants will be able to:

• Identify the vulnerabilities inherent in a standard Kubernetes installation.
• Block unauthenticated access to the Kubernetes API, databases, and other services.
• Safeguard Kubernetes clusters from both accidental and malicious intrusions.
• Develop a comprehensive security policy and implement industry best practices.

This instructor-led, live training in Taichung (online or onsite) is aimed at engineers who wish to advance their knowledge of Docker so as to deploy applications at a larger scale while maintaining control.

By the end of this training, participants will be able to:

• Build their own Docker images.
• Deploy and manager large number of Docker applications .
• Evaluate different container orchestration solutions and choose the most suitable one.
• Set up a continuous integration process for Docker applications.
• Integrate Docker applications with existing continuous tools integration processes.
• Secure their Docker applications.

This intensive 7-day programme offers a comprehensive, hands-on exploration into the design, deployment, and operation of cloud-native applications through modern DevOps practices.

Participants will learn to architect scalable microservices, optimize container environments, and manage production workloads using Kubernetes. The curriculum spans advanced deployment techniques, GitOps-driven automation, and observability standards to guarantee system reliability and high performance.

A significant emphasis is placed on addressing real-world operational challenges, such as incident response, failure simulation, and root cause analysis. The programme concludes by integrating AI-powered tools to enhance troubleshooting capabilities and expedite operational decision-making.

Upon completion, participants will possess a clear understanding of how to build, deploy, monitor, and sustain resilient distributed systems within a Kubernetes-based ecosystem.

This instructor-led, live training in Taichung (online or on-site) is targeted at engineers who want to adopt Docker for deploying and managing software as containers rather than as traditional standalone applications.

Upon completion of this training, participants will be able to:

• Install and configure Docker.
• Understand and implement software containerization.
• Manage Docker-based applications.
• Network different Docker applications and systems.
• Understand and edit Docker registries.

This instructor-led, live training in Taichung (online or onsite) is aimed at intermediate to advanced DevOps engineers and system administrators who wish to deploy and manage self-hosted Kubernetes clusters without cloud dependencies.

By the end of this training, participants will be able to: deploy production-ready Kubernetes clusters using kubeadm on bare-metal or virtual machines; configure high-availability control planes and etcd clusters; implement container networking and storage for self-managed environments; set up monitoring and observability using self-hosted solutions.

In this instructor-led, live training in Taichung, participants will learn the fundamentals of building microservices using Spring Cloud and Docker. Participant knowledge will be assessed through exercises and the step-by-step development of sample microservices.

By the end of this training, participants will be able to:

• Understand the fundamentals of microservices.
• Use Docker to build containers for microservice applications.
• Build and deploy containerized microservices using Spring Cloud and Docker.
• Integrate microservices with discovery services and the Spring Cloud API Gateway.
• Use Docker Compose for end-to-end integration testing.

OpenShift is one of the leading Kubernetes-based platforms for deploying and managing containerized applications in cloud, hybrid and on-premises environments.

This hands-on training teaches participants how to install, administer and troubleshoot OpenShift clusters while applying security, networking and storage best practices. Through practical exercises, participants gain the skills needed to confidently manage production-ready OpenShift environments.

Discover how to develop, deploy, and manage containerized applications with OpenShift, a leading Kubernetes-based platform for cloud-native development. This practical training covers application deployment, containers, networking, CI/CD, and DevOps workflows, providing participants with the skills to build and maintain modern applications in production environments.

This instructor-led, live training in Taichung (online or onsite) is aimed at intermediate-level virtualization administrators who wish to use open-source platforms to migrate away from VMware.

By the end of this training, participants will be able to:

• Install and configure KVM, oVirt, and Proxmox VE.
• Migrate virtual workloads from VMware.
• Implement high availability and disaster recovery.
• Optimize performance in open-source virtualization environments.