Containers and Virtual Machines (VMs) Training in Grenoble

Edge AI represents a paradigm centered on executing machine learning inference near data sources to deliver low-latency, efficient, and scalable processing.

This instructor-led, live training (available online or onsite) is designed for intermediate to advanced practitioners aiming 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 practical edge scenarios.

Format of the Course also allows for the evaluation of participants.

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

Course Customization Options

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

Kubernetes is a container orchestration platform widely used for managing distributed applications at scale.

This instructor-led, live training (online or onsite) is aimed at advanced-level practitioners who wish to apply AI and machine learning techniques to optimize Kubernetes resource usage, scheduling decisions, and autoscaling strategies.

At the completion of this program, participants will be able to:

• Apply AI/ML models to improve workload scheduling decisions in Kubernetes.
• Use predictive analytics to optimize CPU, GPU, and memory allocation.
• Implement intelligent autoscaling using reinforcement learning and metric forecasting.
• Reduce infrastructure cost and latency through automated resource optimization.

Format of the Course also allows for the evaluation of participants.

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

Course Customization Options

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

MLOps on Kubernetes serves as 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, targets intermediate-level practitioners aiming 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:

• Explore the Kubeflow ecosystem and its core components.
• Develop reproducible workflows using Kubeflow Pipelines.
• Execute scalable training jobs within a Kubernetes environment.
• Deploy machine learning models efficiently via Kubeflow Serving.

Format of the Course also allows for the evaluation of participants.

• 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 through continuous integration and delivery pipelines.

This instructor-led live training, available online or onsite, targets intermediate-level professionals aiming to automate end-to-end AI model delivery workflows leveraging 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.
• Implementing version control and ensuring reproducibility throughout the model lifecycle.
• Integrating automated deployment strategies for AI services.
• Applying CI/CD best practices specifically tailored to machine learning operations.

Course Format

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

Course Customization Options

• Should your organization require customized pipeline workflows or specific platform integrations, please contact us to tailor this course accordingly.

This instructor-led, live training in Grenoble (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 both online and onsite, is designed for advanced professionals aiming to design and deploy distributed AI inference systems within heterogeneous environments.

After completing this training, participants will be capable of:

• Developing secure and scalable containerized AI services for multi-site environments.
• Deploying AI inference workloads to cloud platforms, local servers, and edge devices using Docker.
• Integrating orchestration tools to automate distributed AI operations.
• Enhancing inference latency, reliability, and resilience across diverse infrastructures.

Course Format

• Guided presentations and discussions led by experts.
• Ample hands-on practice and applied exercises.
• Real-world experimentation within a controlled live-lab environment.

Customization Options

• For personalized adjustments to align this course with your organization's infrastructure or specific use cases, please contact us to arrange custom training.

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

This instructor-led live training, available either online or onsite, is designed for beginner to intermediate IT professionals who want to master the core concepts and components of Kubernetes and utilize it to manage containerized applications at scale.

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

• Comprehend the architecture and components of Kubernetes.
• Deploy and manage containers within a Kubernetes cluster.
• Configure networking, storage, and scaling for various workloads.
• Troubleshoot common issues and adhere to best practices for cluster operations.

Course Format

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

Course Customization Options

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

This instructor-led, live training in Grenoble (online or onsite) is aimed at DevOps engineers and developers who wish to use 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 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, available online or on-site, targets technical professionals at the beginner to intermediate level who aim to develop secure, portable AI inference microservices deployable across local machines, servers, or cloud virtual machines.

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

• Build lightweight inference containers for both local and cloud deployment.
• Secure containerized AI services by applying industry best practices.
• Implement portable microservice workflows to ensure consistent environments.
• Deploy AI inference endpoints across diverse infrastructures.

Course Format

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

Course Customization Options

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

In this instructor-led, live training in Grenoble (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.

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 technical professionals who want to configure, optimize, and run GPU-enabled AI workloads within Docker containers.

Upon completing this course, participants will be able to:

• Create and operate GPU-enabled containers for both training and inference.
• Set up 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.

Course Format

• 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 Grenoble (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 Grenoble (online or onsite) is designed for intermediate to advanced developers, DevOps professionals, and architects who aim to design, deploy, and manage resilient applications utilizing 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 Grenoble (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.

Gain foundational knowledge of containers, Kubernetes, and OpenShift through practical, hands-on training designed for developers, DevOps engineers, and IT professionals. Participants will master the techniques to build containerized applications, deploy workloads, manage Kubernetes resources, and leverage OpenShift to streamline modern application delivery across cloud and hybrid environments.

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

This instructor-led training session, available either online or onsite, targets technical professionals with intermediate to advanced skills who aim to containerize and operationalize complete ML pipelines using Docker.

After completing this training, participants will be equipped to:

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

Course Format

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

Course Customization Options

• For training tailored to specific ML infrastructure requirements, please contact us to explore available options.

Docker serves as a containerization platform that facilitates consistent, portable, and reproducible environments for AI and machine learning workloads.

This instructor-led live training, available either online or on-site, targets intermediate-level professionals aiming 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 dependencies.
• Optimize Docker environments for both performance and portability.
• Deploy containerized ML services across various runtime environments.

Course Format

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

Course Customization Options

• To tailor this training to your organizational needs, please contact us to arrange a session.

This instructor-led live training in Grenoble (online or onsite) 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 Grenoble (online or onsite) is designed for intermediate-level developers and DevOps engineers who wish to integrate Minikube into their development workflows.

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

• Set up and manage a local Kubernetes environment using Minikube.
• Deploy, manage, and debug applications on Minikube effectively.
• Integrate Minikube into continuous integration and deployment pipelines.
• Leverage Minikube's advanced features to streamline their development process.
• Apply industry best practices for local Kubernetes development.

This instructor-led, live training in Grenoble (online or onsite) is aimed at intermediate-level developers and DevOps engineers who wish 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 Grenoble (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 fundamental concepts and architecture of Kubernetes.
• Deploy and manage containers using kubectl and the Minikube dashboard.
• Set up persistent storage and networking solutions for Kubernetes.
• Leverage Minikube for developing, testing, and debugging applications.

Training Objectives: Gain theoretical and practical skills in Docker and Kubernetes.

In this instructor-led live training in Grenoble (onsite or remote), participants will learn how to create and manage Docker containers, then deploy a sample application inside a container. Participants will also learn how to automate, scale, and manage their containerized applications within a Kubernetes cluster. Finally, the training goes on to more advanced topics, walking 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 become a leading platform for container orchestration.

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

Since 2019, we have also been helping our customers validate their performance in Kubernetes environments by preparing them and encouraging them to pass the CKA and CKAD exams.

This instructor-led, live training (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 lectures and discussions.
• Ample 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 Grenoble (online or onsite), participants will learn how to set up and manage a production-scale 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 AKS cloud.
• Integrate Kubernetes with third-party continuous integration (CI) software.
• Ensure high availability and disaster recovery in Kubernetes.

Master the skills to build, deploy, and administer containerized environments using Docker, Kubernetes, and OpenShift. This intensive five-day, hands-on training covers container images, Kubernetes workloads, cluster networking, storage solutions, security protocols, monitoring techniques, and practical OpenShift administration. Participants will acquire the necessary competencies to operate modern container platforms effectively and troubleshoot applications across both development and production environments.

This instructor-led, live training (online or onsite) is designed for engineers seeking to automate, secure, and monitor containerized applications within a large-scale Kubernetes cluster.

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

• Utilize Kubernetes to deploy and manage distinct environments within a single cluster
• Enhance the security, scalability, and monitoring capabilities of a Kubernetes cluster

Course Format

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

Customization Options

• To request a customized version of this course, please contact us to make arrangements.

The Certified Kubernetes Application Developer (CKAD) program was created by The Linux Foundation and the Cloud Native Computing Foundation (CNCF), the host organization for Kubernetes.

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

Furthermore, the training emphasizes gaining practical experience in Kubernetes application development; therefore, we recommend participating even if you do not plan to take the CKAD exam.

NobleProg has been providing Docker & Kubernetes training since 2015. With over 360 successfully completed training projects, we have become one of the most recognized training providers worldwide in the field of containerization. Since 2019, we have also been assisting our customers in validating their performance in k8s environments by preparing them and encouraging them to pass the CKA and CKAD exams.

Course Format

• Interactive lectures and discussions.
• Abundant 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 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 Grenoble 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 designed for Kubernetes, enabling secure, observable, and manageable communication between microservices. By utilizing Istio's Envoy-based sidecar proxies, teams can enforce policies, secure communications via mTLS, gain deep traffic visibility, 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 able to:

• Install and configure Istio on Kubernetes clusters.
• Understand 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 using Prometheus, Grafana, and Jaeger.
• Integrate 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.

Customization Options

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

The advancement of microservices and containers in recent years has profoundly transformed the way we design, develop, deploy, and operate software. Contemporary applications must be optimized for scalability, elasticity, fault tolerance, and adaptability to change. Meeting these new demands requires modern architectures to adopt a distinct set of patterns and practices. This training explores methods to identify, comprehend, and adapt to these evolving requirements.

Audience
This training is designed for individuals who have a basic understanding of container technology and Kubernetes concepts but may lack practical, hands-on experience. The curriculum is grounded in use cases and lessons learned from real-world projects, aiming to inspire participants to build and manage more effective cloud-native applications.

• Developers
• Operations personnel
• DevOps engineers
• QA Engineers
• IT Project Managers

Course Format

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

Course Customization Options

• To request a customized training session for this course, please contact us to make arrangements.

This instructor-led, live training in Grenoble (online or onsite) is designed for engineers looking to utilize Helm to streamline the installation and management of Kubernetes applications.

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

• Install and configure Helm.
• Create reproducible builds for Kubernetes applications.
• Distribute applications as Helm charts.
• Run third-party applications stored as Helm charts.
• Manage Helm package releases.

This instructor-led, live training in Grenoble (online or onsite) is aimed at DevOps engineers who wish 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 Grenoble (online or onsite) is designed for Kubernetes practitioners aiming 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 in Grenoble (online or onsite) is designed for developers and DevOps engineers who wish to apply a serverless approach to building enterprise applications using Kubernetes.

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

• Set up and configure the Kubernetes system to begin 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.
• Practice their skill in the Python programming language and apply it to implement serverless systems.
• Secure enterprise applications deployed through a serverless framework on Kubernetes.
• Utilize modern cloud computing methods to optimize DevOps task processing workflows.

This instructor-led live training in Grenoble (online or onsite) targets engineers who want to secure Kubernetes clusters beyond the default security settings.

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

• Understand the vulnerabilities exposed by a default Kubernetes installation.
• Prevent unauthenticated access to the Kubernetes API, database, and other services.
• Protect a Kubernetes cluster from accidental or malicious access.
• Put together a comprehensive security policy and set of best practices.

This instructor-led, live training in Grenoble (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 practical, in-depth exploration of designing, deploying, and managing cloud-native applications through contemporary DevOps methodologies.

Attendees will learn to architect scalable microservices, optimize containerized environments, and manage production workloads within Kubernetes. The curriculum encompasses advanced deployment techniques, GitOps-driven automation, and observability strategies essential for ensuring system reliability and performance.

Significant emphasis is placed on addressing real-world operational challenges, such as incident management, failure simulation, and root cause analysis. The programme culminates with the application of 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 ecosystem.

This instructor-led, live training in Grenoble (online or onsite) is designed for engineers who wish to use Docker to deploy and manage software as containers instead of as traditional stand-alone 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 Grenoble (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 Grenoble, participants will learn the essentials of building microservices using Spring Cloud and Docker. Their understanding will be tested 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 using 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 necessary to build and maintain modern applications in production environments.

This instructor-led, live training in Grenoble (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.