Containers and Virtual Machines (VMs) Training in Toluca

Edge AI focuses on executing machine learning inference near data sources to ensure low latency, efficiency, and scalability.

This instructor-led live training, available online or onsite, targets intermediate to advanced practitioners seeking 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 for edge deployments.
• Effectively deploy AI inference workloads on constrained edge nodes.
• Address connectivity challenges and synchronization patterns.
• Optimize performance, storage, and networking for practical edge scenarios.

Course Format

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

Course Customization Options

• For customized training tailored to your specific edge platform needs, please contact us to arrange.

Kubernetes is a widely adopted container orchestration platform designed to manage distributed applications at scale.

This instructor-led live training, available online or onsite, is designed for advanced practitioners who want 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 in Kubernetes.
• Utilize predictive analytics to optimize CPU, GPU, and memory allocation.
• Implement intelligent autoscaling using reinforcement learning and metric forecasting.
• Lower infrastructure costs and latency through automated resource optimization.

Course Format

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

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 that build 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 structured methodology for automating the packaging, testing, containerization, and deployment of models by leveraging continuous integration and continuous delivery pipelines.

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

Upon completion of the training, participants will be capable of:

• Establishing automated pipelines for constructing and testing AI model containers.
• Enforcing version control and reproducibility throughout the model lifecycle.
• Integrating automated deployment strategies for AI services.
• Applying CI/CD best practices specifically adapted for machine learning operations.

Course Format

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

Course Customization Options

• If your organization requires customized pipeline workflows or specific platform integrations, please contact us to tailor this course to your needs.

This instructor-led, live training in Toluca (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-premises, and edge environments through unified, container-based workflows.

This instructor-led live training (available online or onsite) targets advanced professionals seeking to design and deploy distributed AI inference systems within heterogeneous environments.

Upon completing this training, participants will be able to:

• Develop 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.
• Enhance 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

• For tailored adjustments to align this course with your organization’s infrastructure or specific use cases, please contact us to customize the training.

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

This instructor-led live training (available online or onsite) targets beginner to intermediate-level IT professionals eager to master the core concepts and components of Kubernetes, enabling them to manage containerized applications at scale.

Upon completing this training, participants will be able to:

• Grasp the Kubernetes architecture and its key components.
• Deploy and manage containers within a Kubernetes cluster.
• Configure networking, storage, and scaling for various workloads.
• Troubleshoot common issues and apply best practices for cluster operations.

Course Format

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

Customization Options

• To request a customized version of this course, please contact us to arrange details.

This live, instructor-led training Toluca (online or on-site) is designed for DevOps engineers and developers who want to utilize Kubernetes to construct, deploy, and manage containers and cluster components in a secure and scalable setting.

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

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

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

This instructor-led, live training session (available online or on-site) targets beginner to intermediate technical professionals looking to develop secure, portable AI inference microservices that can be consistently deployed across local machines, servers, or cloud virtual machines.

Upon completing this workshop, participants will be able to:

• Create lightweight inference containers for both local and cloud deployment.
• Enhance the security of containerized AI services by applying industry best practices.
• Implement portable microservice workflows to ensure consistent environments.
• Deploy AI inference endpoints across various infrastructure types.

Course Format

• Guided lectures complemented by practical demonstrations.
• Hands-on exercises to reinforce deployment and security techniques.
• Live-lab practice for building and running portable inference services.

Course Customization Options

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

In this instructor-led, live training in Toluca (onsite or remote), participants will learn how to deploy a collection of sample servers inside containers, then automate, scale, and manage their containerized servers within a Kubernetes cluster. The training goes on to more advanced topics, walking participants through the process of securing, networking and monitoring 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.

Utilizing GPU acceleration is critical for executing high-performance deep learning tasks in a scalable and efficient way.

This instructor-led live training, available online or onsite, targets intermediate-level technical professionals looking to configure, optimize, and run GPU-enabled AI workloads within Docker containers.

Upon completing this course, participants will be equipped to:

• Create and operate GPU-enabled containers for training and inference.
• Configure CUDA, drivers, and runtime libraries for AI workflows in containers.
• Optimize resource allocation and isolation for applications that heavily utilize GPUs.
• Deploy scalable, containerized deep learning services in production settings.

Course Format

• Interactive instruction reinforced with real-world demonstrations.
• Practice exercises focused on developing with GPU support.
• Hands-on implementation within a live laboratory environment.

Course Customization Options

• For customized training that aligns with your infrastructure or GPU stack, please contact us to make arrangements.

This instructor-led, live training in Toluca (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 Toluca (online or onsite) is designed for intermediate to advanced developers, DevOps professionals, and architects who want 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 using 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 Toluca (online or onsite) is designed for advanced-level platform engineers and DevOps professionals who want to master application scaling 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 create reproducible, portable, and scalable environments for machine learning systems.

This instructor-led live training (available online or on-site) is designed for intermediate to advanced technical professionals seeking to containerize and operationalize complete ML pipelines using Docker.

After completing this training, participants will be able to:

• Containerize workloads for ML training, validation, and inference.
• Design and orchestrate end-to-end ML pipelines leveraging Docker and complementary tools.
• Implement version control, reproducibility, and CI/CD practices for ML components.
• Deploy, monitor, and scale ML services within containerized environments.

Course Format

• Interactive lectures complemented by practical demonstrations.
• Hands-on exercises centered on constructing real-world ML pipeline components.
• Live-lab implementation of end-to-end containerized workflows.

Customization Options

• For tailored training aligned with specific ML infrastructure requirements, please reach out to discuss available options.

Docker serves as a containerization platform that facilitates the creation of consistent, portable, and reproducible environments specifically designed for AI and machine learning tasks.

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

Upon finishing this course, participants will be capable of:

• Creating and managing Docker images customized for AI and ML applications.
• Containerizing machine learning pipelines, tools, and dependencies.
• Optimizing Docker environments to enhance performance and portability.
• Deploying containerized ML services across various runtime environments.

Course Format

• Concept demonstrations complemented by guided discussions.
• Practical exercises centered on real-world containerization challenges.
• Hands-on implementation using live Docker lab environments.

Customization Options

• To tailor this training to your organization's specific needs, please reach out to us to arrange a session.

This instructor-led live training in Toluca (online or on-site) is aimed at beginner-level developers who wish to learn the basics of Kubefirst and how it simplifies, secures, and accelerates 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 Toluca (online or onsite) is designed for intermediate developers and DevOps engineers looking to integrate Minikube into 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 Toluca (online or onsite) targets intermediate developers and DevOps engineers looking to build, deploy, and manage microservices using Spring Cloud and Docker.

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

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

This instructor-led, live training in Toluca (online or onsite) is aimed at beginner-level to intermediate-level software developers and DevOps professionals who wish 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: Gain both theoretical and practical skills in Docker and Kubernetes.

In this instructor-led, live training in Toluca (onsite or remote), participants will learn how to create and manage Docker containers, followed by deploying a sample application inside a container. Participants will also learn how to automate, scale, and manage their containerized applications within a Kubernetes cluster. Furthermore, the training covers advanced topics, guiding participants through the process of securing, scaling, and monitoring a Kubernetes cluster.

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

• Set up and run a Docker container.
• Deploy a containerized server and web application.
• Build and manage Docker images.
• Set up a Docker and Kubernetes cluster.
• Use 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 the leading platform for container orchestration.

Since 2015, NobleProg has specialized in delivering Docker and Kubernetes training. With over 360 successful training projects completed, we have established ourselves as one of the most recognized training providers globally in the field of containerization.

Since 2019, we have also been assisting our clients in validating their Kubernetes skills by preparing them for and encouraging the pursuit of 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 expertise by passing the CKA exam.

Furthermore, the training emphasizes gaining practical experience in Kubernetes Administration. Therefore, we highly recommend participating, even if you do not plan to take the CKA exam.

Course Format

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

Course Customization Options

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

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

Upon completing this training, participants will be able to:

• Configure and manage Kubernetes on AKS.
• Deploy, administer, and scale a Kubernetes cluster.
• Deploy containerized (Docker) applications on Azure.
• Migrate an existing on-premise Kubernetes environment to the AKS cloud.
• Integrate Kubernetes with third-party continuous integration (CI) software.
• Ensure high availability and disaster recovery within 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 online or on-site) is designed for engineers who want to automate, secure, and monitor containerized applications within large-scale Kubernetes clusters.

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

• Use Kubernetes to deploy and manage different environments under the same cluster
• Secure, scale and monitor a Kubernetes cluster

Format of the Course

• Interactive lecture and discussion
• Lots of exercises and practice
• 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 Certified Kubernetes Application Developer (CKAD) program was created by The Linux Foundation and the Cloud Native Computing Foundation (CNCF), the organization behind Kubernetes.

This instructor-led live training (available online or in-person) is designed for Developers who want to validate their abilities in designing, building, configuring, and exposing cloud-native applications on Kubernetes.

Additionally, the training emphasizes gaining hands-on experience in Kubernetes application development; therefore, we encourage participation 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 been helping our customers validate their performance in Kubernetes environments by preparing them to pass the CKA and CKAD exams.

Course Format

• Interactive lectures and discussions.
• 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 make arrangements.
• To learn more about CKAD, please visit: https://training.linuxfoundation.org/certification/certified-kubernetes-application-developer-ckad/

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

Istio operates as an open-source service mesh within Kubernetes environments, enabling secure, observable, and manageable connectivity between microservices. By utilizing Istio’s Envoy-based sidecar proxies, teams can enforce policies, secure communications via mTLS, gain deep visibility into traffic patterns, and enhance reliability at scale.

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

Upon completing this training, participants will be equipped to:

• Install and configure Istio on Kubernetes clusters.
• Comprehend and apply service mesh concepts, including traffic management, security, and observability.
• Deploy microservices applications within an Istio service mesh.
• Secure service-to-service communications using mutual TLS (mTLS) and Zero Trust principles.
• Monitor, trace, and troubleshoot microservices with Prometheus, Grafana, and Jaeger.
• Integrate Istio with Calico for advanced network policies and security.

Format of the Course

• 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 has profoundly transformed how we architect, develop, deploy, and operate software. Today's applications demand optimization for scalability, elasticity, fault tolerance, and adaptability. To meet these evolving needs, modern architectures necessitate a distinct set of patterns and best practices. This training explores methods to identify, comprehend, and adapt to these contemporary requirements.

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

• Developers
• Operations staff
• DevOps professionals
• QA Engineers
• IT Project Managers

Course Format

• Interactive lectures and discussions
• Extensive exercises and practical applications
• Hands-on implementation within a live-lab environment

Course Customization Options

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

This instructor-led, live training in Toluca (online or onsite) is designed for engineers looking to use 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.
• Share applications as Helm charts.
• Run third-party applications saved as Helm charts.
• Manage releases of Helm packages.

This instructor-led, live training in Toluca (online or onsite) is targeted at DevOps engineers aiming to use Kubernetes and GitLab to automate the DevOps lifecycle.

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 Toluca (online or onsite) is aimed at Kubernetes practitioners who wish to prepare for the CKS exam.

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

This instructor-led, live training in Toluca (online or onsite) is designed for 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 to serverless development and integrate it with Kubernetes components.
• Practice their skill in Python programming language and apply it 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 Toluca (online or onsite) is designed for engineers who want to enhance the security of their Kubernetes cluster beyond the default settings.

Upon completing this training, participants will be able to:

• Identify the vulnerabilities associated with a default Kubernetes installation.
• Block unauthenticated access to the Kubernetes API, database, and other services.
• Safeguard the Kubernetes cluster from both accidental and malicious intrusions.
• Develop a comprehensive security policy along with a set of best practices.

This instructor-led, live training in Toluca (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 7-day program offers a thorough, practical exploration of designing, deploying, and managing cloud-native applications using contemporary DevOps methodologies.

Attendees will learn how to architect scalable microservices systems, optimize container environments, and oversee production workloads with Kubernetes. The curriculum addresses advanced deployment techniques, GitOps-driven automation, and observability strategies to guarantee system reliability and performance.

Great emphasis is placed on addressing real-world operational hurdles, such as incident response, failure simulation, and root cause analysis. The program wraps up by leveraging AI-powered tools to aid in troubleshooting and speed up operational decision-making.

Upon completion of the training, participants will possess a clear grasp of how to build, deploy, monitor, and sustain resilient distributed systems within a Kubernetes environment.

This instructor-led live training in Toluca (available online or on-site) is aimed at engineers who want to utilize Docker to deploy and manage software as containers instead of traditional standalone software.

By the end 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 Toluca (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 Toluca, participants will learn the fundamentals of building microservices using Spring Cloud and Docker. Participant knowledge is put to the test 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.

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

This instructor-led, live training in Toluca (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.