Containers and Virtual Machines (VMs) Training in Tashkent

Edge AI is a paradigm focused on running machine learning inference close to data sources to achieve low-latency, efficient, and scalable processing.

This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level practitioners who wish to deploy, orchestrate, and optimize AI workloads on Kubernetes-based edge environments.

By completing this course, participants will be able to:

• Set up lightweight Kubernetes distributions for edge deployments.
• Deploy AI inference workloads effectively across constrained edge nodes.
• Manage connectivity challenges and synchronization patterns.
• Optimize performance, storage, and networking for real-world edge scenarios.

Format of the Course

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

Course Customization Options

• To request a customized training tailored to your edge platform needs, 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

• 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 is a framework for automating the training, validation, packaging, and deployment of machine learning models using containerized pipelines and GitOps workflows.

This instructor-led, live training (online or onsite) is aimed at intermediate-level practitioners who wish to build automated, scalable MLOps pipelines on Kubernetes.

Upon completing this training, participants will be equipped to:

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

Course Format

• Instructor-led presentations and in-depth technical sessions.
• Practical exercises for building real-world CI/CD workflows.
• Live lab sessions for deploying ML workloads on Kubernetes.

Course Customisation Options

• Organisations may request customised 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 involves a systematic method for automating the packaging, testing, containerization, and deployment of AI models through continuous integration and continuous delivery pipelines.

This instructor-led, live training (available online or onsite) is designed for intermediate-level professionals who want to automate end-to-end AI model delivery workflows using Docker and CI/CD platforms.

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

• Build automated pipelines for constructing and testing AI model containers.
• Establish version control and reproducibility for model lifecycles.
• Incorporate automated deployment strategies for AI services.
• Apply CI/CD best practices customized 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

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

This instructor-led, live training in Tashkent (online or onsite) is designed for advanced-level Kubernetes administrators and DevOps engineers who aim to enhance their monitoring expertise 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 tasks 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 architect and deploy distributed AI inference systems in heterogeneous settings.

After completing this training, participants will be able to:

• Create secure and scalable containerized AI services for multi-site environments.
• 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 setup.

Course Customization Options

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

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

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

Upon completing 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 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 in a live-lab environment.

Customization Options

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

This live, instructor-led training conducted in Tashkent (online or onsite) is designed for DevOps engineers and developers who aim to leverage Kubernetes for building, deploying, and managing containers and cluster components within secure and scalable environments.

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 (available online or onsite) targets beginner to intermediate-level technical professionals who aim to develop secure, portable AI inference microservices that can be consistently deployed across local machines, servers, or cloud VMs.

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

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

Format of the Course

• Guided lectures paired with 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 tailor this training to your specific infrastructure or AI tooling stack, please contact us to arrange.

In this instructor-led, live training Tashkent (onsite or remote), participants will learn to deploy sample servers within containers, followed by automating, scaling, and managing these servers in a Kubernetes cluster. The training advances to sophisticated topics, guiding participants through the processes 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 essential for running high-performance deep learning workloads in a scalable and efficient manner.

This instructor-led, live training (online or onsite) is aimed at intermediate-level technical professionals who wish to configure, optimize, and run GPU-enabled AI workloads inside Docker containers.

At the conclusion 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.

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 Tashkent (online or onsite) is aimed at intermediate-level professionals who wish to effectively deploy, manage, and scale containerised 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 customise 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.
• Analyse and troubleshoot Kubernetes issues.
• Deploy resources efficiently using Helm Charts.

This instructor-led, live training in Tashkent (online or on-site) is designed for intermediate to advanced-level developers, DevOps professionals, and architects who aim 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 Tashkent (available online or onsite) is designed for advanced-level platform engineers and DevOps professionals who aim 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.
• Utilise Helm charts for efficient service deployment.
• Monitor and maintain the health of microservices in production environments.
• Apply best practices for security and compliance within a Kubernetes environment.

This training provides a foundational overview of containers, key Kubernetes principles, and OpenShift platform concepts, emphasizing practical, hands-on experience.

Delivered as an instructor-led live session (available online or in-person), this course is designed for beginner to intermediate technical professionals seeking to master container workflows, understand Kubernetes primitives, and learn how to deploy and manage applications on OpenShift.

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

• Constructing and managing container images and registries while adhering to best practices for security and reproducibility.
• Deploying and controlling Kubernetes resources such as pods, deployments, and services within OpenShift.
• Leveraging OpenShift capabilities—including routes, buildConfigs, and the web console—to accelerate application delivery.
• Setting up persistent storage, configuration management, and secret handling for stateful workloads.
• Applying fundamental security measures, RBAC policies, and monitoring techniques to ensure cluster health and application reliability.

Course Delivery Format

• Engaging interactive lectures and group discussions.
• Daily hands-on labs conducted in a live OpenShift environment.
• Practical exercises based on real-world scenarios, along with troubleshooting workshops.

Customization Opportunities

• To arrange a tailored version of this course, please reach out to us for customization details.

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

This instructor-led live training, available both online and on-site, targets intermediate to advanced technical professionals seeking to containerize and operationalize complete ML pipelines using Docker.

Upon completing 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 complementary tools.
• Implement versioning, reproducibility, and CI/CD practices for ML components.
• Deploy, monitor, and scale ML services within containerized environments.

Course Format

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

Customization Options

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

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

This instructor-led live training, available both online and on-site, is designed for intermediate-level professionals aiming to package machine learning codebases, dependencies, and models using Docker to ensure reliable workflows from development to production.

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

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

Course Format

• Concept demonstrations reinforced by guided discussions.
• Practical exercises focused on real-world containerization scenarios.
• Hands-on implementation using live-lab Docker environments.

Customization Options

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

This instructor-led live training Tashkent (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 Tashkent (online or onsite) is designed for intermediate-level developers and DevOps engineers who want to utilize Minikube as 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 Tashkent (online or on-site) is designed for 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 guided, live training in Tashkent (online or on-site) is tailored for software developers and DevOps practitioners at beginner to intermediate levels who aim to master the setup and management of a local Kubernetes environment via Minikube.

Upon completing this training, participants will be equipped to:

• Install and configure Minikube on their local workstations.
• Grasp the fundamental concepts and architectural structure of Kubernetes.
• Deploy and oversee containers utilizing kubectl and the Minikube dashboard.
• Implement persistent storage and networking configurations for Kubernetes.
• Leverage Minikube for application development, testing, and debugging.

Training objectives: Acquire both theoretical and practical skills in Docker and Kubernetes.

Through this instructor-led live training in Tashkent (onsite or remote), participants will acquire the skills to create and manage Docker containers, as well as deploy a sample application within one. The curriculum also covers automating, scaling, and managing containerized applications inside a Kubernetes cluster. Furthermore, the training progresses to advanced topics, guiding participants through securing, scaling, and monitoring a Kubernetes cluster.

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

• Set up and operate a Docker container.
• Deploy a containerized server and web application.
• Construct and oversee Docker images.
• Establish 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 in collaboration with the Cloud Native Computing Foundation (CNCF).

Kubernetes has become the leading platform for container orchestration.

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 assisting our customers in validating their performance in Kubernetes environments by preparing them to take and 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. Therefore, we recommend participating even if you do not intend to take the CKA exam.

Course Format

• Interactive lectures and discussions.
• 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 held at Tashkent (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.

In this instructor-led, live training in Tashkent, participants will learn how to manage Red Hat OpenShift Container Platform.

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

• Create, configure, manage, and troubleshoot OpenShift clusters.
• Deploy containerized applications on-premise, in public cloud or on a hosted cloud.
• Secure OpenShift Container Platform
• Monitor and gather metrics.
• Manage storage.

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

Upon completing this training, participants will be able to:

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

Course Format

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

Course Customization Options

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

The Certified Kubernetes Application Developer (CKAD) program has been developed by The Linux Foundation and the Cloud Native Computing Foundation (CNCF), the host of Kubernetes.

This instructor-led, live training (online or onsite) is aimed at Developers who wish to confirm their skills in design, build, configure, and expose cloud native applications for Kubernetes.

On the other hand, the training also focuses on gaining practical experience in Kubernetes application development, so we recommend participating even if you do not intend to take the CKAD exam.

NobleProg has been delivering Docker & Kubernetes training since 2015. With more than 360 successfully completed training projects, we have become one of the best-known training companies worldwide in the field of containerization. Since 2019, we have also been helping our customers validate their performance in k8s environments by preparing them and encouraging them to pass CKA and CKAD exams.

Course Format

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

Course Customization Options

• To request a customized version of 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 Tashkent provides participants with an overview of Rancher and demonstrates through hands-on practice how to deploy and manage a Kubernetes cluster with Rancher.

Istio serves as an open-source service mesh designed for Kubernetes environments, facilitating secure, visible, and manageable connections between microservices. Utilizing Istio's Envoy-based sidecar proxies, engineering teams can implement policy enforcement, secure inter-service communications via mTLS, achieve comprehensive traffic visibility, and enhance reliability at scale.

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

Upon completing this training, participants will be capable of:

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

Course Format

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

Course Customization Options

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

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

Audience
This training is designed for individuals who possess a basic familiarity with container technology and Kubernetes concepts but may lack practical, real-world experience. It is grounded in use cases and lessons learned from actual projects, aiming to inspire participants to create and manage even more effective cloud-native applications.

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

Course Format

• Interactive lectures and discussions
• Numerous 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 Tashkent (online or onsite) is aimed at engineers who wish to use Helm to streamline the process of installing and managing Kubernetes applications.

By the end 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 Tashkent (online or onsite) targets DevOps engineers who want 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 Tashkent (available online or on-site) is designed for Kubernetes practitioners aiming to prepare for the CKS exam.

By the end of this training, participants will be able to secure Kubernetes environments and container-based applications across all stages of an application's life cycle: build, deployment, and runtime.

This instructor-led live training in Tashkent (available online or onsite) is designed for developers and DevOps engineers who aim to utilize a serverless approach for building enterprise applications on Kubernetes.

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

• Set up and configure the Kubernetes system to commence development using a serverless architecture.
• Understand the fundamental concepts and principles of serverless environments.
• Operate the toolchains required for serverless development and integrate them with Kubernetes components.
• Apply Python programming skills 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 Tashkent (online or onsite) is designed for engineers seeking to secure their Kubernetes cluster beyond the default configuration.

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

• Identify vulnerabilities present in a standard Kubernetes installation.
• Block unauthenticated access to the Kubernetes API, database, and other services.
• Protect the cluster from both accidental and malicious intrusions.
• Develop a comprehensive security policy and adopt industry best practices.

This instructor-led, live training in Tashkent (online or on-site) is intended for engineers who wish to deepen their Docker expertise to deploy applications at scale while maintaining full control.

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

• Build their own Docker images.
• Deploy and manage large numbers of Docker applications.
• Evaluate various container orchestration solutions and select the most appropriate one.
• Establish a continuous integration pipeline for Docker applications.
• Integrate Docker applications with existing continuous integration tooling.
• Secure their Docker applications.

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

• Creating custom Docker images.
• Deploying and managing a large number of Docker applications.
• Evaluating various container orchestration solutions to select the most appropriate one.
• Establishing a continuous integration process for Docker applications.
• Integrating Docker applications with existing continuous integration toolchains.
• Implementing security measures for Docker applications.
• Utilizing Kubernetes to deploy and manage diverse environments within a single cluster.
• Securing, scaling, and monitoring a Kubernetes cluster.

This intensive 7-day program offers a comprehensive, practical exploration into the design, deployment, and operation of cloud-native applications through contemporary DevOps methodologies.

Participants will learn to architect scalable microservices solutions, optimize containerized environments, and manage production-grade workloads using Kubernetes. The curriculum includes advanced deployment techniques, GitOps-driven automation, and robust observability frameworks to guarantee system stability and high performance.

Emphasis is placed on addressing real-world operational hurdles, such as incident management, failure simulation, and root cause analysis. The program wraps up by leveraging AI-enabled tools to enhance troubleshooting capabilities and expedite operational decision-making processes.

Upon completion, participants will possess a solid grasp of how to construct, deploy, monitor, and sustain resilient distributed systems within a Kubernetes ecosystem.

This instructor-led, live training in Tashkent (online or onsite) is aimed at 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.
• Managing Docker based applications.
• Network different Docker applications and systems.
• Understand and edit Docker registries.

This instructor-led, live training in Tashkent (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.

Upon completion of this professional course, participants will have acquired the knowledge and skills necessary to effectively implement, manage, and scale the Kuma service mesh within Kubernetes environments. They will be capable of deploying advanced traffic routing, observability, and security features to optimize the performance and reliability of their microservices-based applications.

In this instructor-led, live training in Tashkent, participants will learn the fundamentals of building microservices using Spring Cloud and Docker. Their knowledge will be tested through hands-on exercises and the step-by-step development of sample microservices.

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

• Understand the core principles 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 Container Platform v4 is a Kubernetes platform built for enterprise use by Red Hat, designed to automate the deployment, scaling, and management of containerized applications.

This instructor-led, live training (online or onsite) is aimed at system administrators and DevOps engineers who wish to deploy, manage, and operate OpenShift clusters in production environments.

The course provides a hands-on approach to installing and managing OpenShift 4 clusters, working with Operators, securing workloads, and operating applications at scale.

Red Hat OpenShift Administration II: Configuring a Production Cluster (DO280) prepares OpenShift Cluster Administrators to carry out daily administration tasks on clusters hosting applications from internal teams and external vendors. It enables self-service for cluster users across different roles and supports the deployment of applications requiring special permissions, such as CI/CD tooling, performance monitoring, and security scanners. The course focuses on configuring multi-tenancy and security features in OpenShift, as well as managing OpenShift add-ons based on operators.

In this instructor-led, live training in Tashkent (online or onsite), participants will learn how to create, update, and maintain applications using OpenShift Container Platform.

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

• Understand OCI (Open Containers Initiative) and its implications for using container engines such as Docker in OpenShift.
• Understand the relationship between the different versions of OpenShift (OKD, OpenShift Container Platform, Red Hat OpenShift, etc.).
• Automate the software delivery pipeline.
• Apply DevOps principles to deliver software continuously.

This instructor-led, live training in Tashkent (online or onsite) targets intermediate-level virtualization administrators who wish to leverage 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.