Building Microservices with Spring Cloud and Docker Training Course

This instructor-led, live training in Uzbekistan (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.

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.

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.

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

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 training in Uzbekistan (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.

In this instructor-led, live training in Uzbekistan, 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.

Through this instructor-led live training in Uzbekistan (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.

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.

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

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.

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.

This instructor-led, live training in Uzbekistan (online or onsite) is tailored for intermediate-level Java developers aiming to design, develop, deploy, and maintain microservices-based applications using Java frameworks like Spring Boot and Spring Cloud.

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

• Understand the principles and benefits of microservices architecture.
• Build and deploy microservices using Java and Spring Boot.
• Implement service discovery, configuration management, and API gateways.
• Secure, monitor, and scale microservices effectively.
• Deploy microservices using Docker and Kubernetes.

This instructor-led, live training in Uzbekistan (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.