Building Microservices with Spring Cloud and Docker Training Course

This instructor-led, live training in Slovakia (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:

• Create their own Docker images.
• Deploy and manage a 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.

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

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

Upon completing this course, participants will be capable of:

• Building and managing Docker images specifically tailored for AI and ML applications.
• Containerizing machine learning pipelines, tools, and dependencies.
• Optimizing Docker environments for improved performance and portability.
• Deploying containerized ML services across diverse runtime environments.

Format of the Course

• Concept demonstrations complemented by guided discussion.
• Hands-on exercises centered on real-world containerization tasks.
• Practical implementation within live-lab Docker environments.

Course Customization Options

• To tailor this training to your organizational needs, please reach out to us to make arrangements.

CI/CD for AI offers 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 on-site, targets intermediate-level professionals seeking to automate end-to-end AI model delivery workflows using Docker and CI/CD platforms.

Upon completing the training, participants will be able to:

• Establish automated pipelines for building and testing AI model containers.
• Implement version control and reproducibility standards for model lifecycles.
• Integrate automated deployment strategies for AI services.
• Apply CI/CD best practices specifically tailored to 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 platform integrations, please contact us to tailor this course.

The Certified Kubernetes Administrator (CKA) certification program was established by The Linux Foundation in partnership with the Cloud Native Computing Foundation (CNCF).

Kubernetes has become the dominant platform for container orchestration.

Since 2015, NobleProg has been providing Docker and Kubernetes training. With over 360 successfully completed training projects, we have established ourselves as one of the leading training providers globally in the field of containerization.

Since 2019, we have also assisted our clients in validating their expertise in Kubernetes environments by preparing them to successfully 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 attending, 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 in a live-lab environment.

Course Customization Options

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

The Certified Kubernetes Application Developer (CKAD) certification program was established by The Linux Foundation and the Cloud Native Computing Foundation (CNCF), the organization responsible for hosting Kubernetes.

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

Additionally, 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 delivering Docker & Kubernetes training since 2015. With over 360 successfully completed training projects, we have become one of the most well-known training providers globally in the field of containerization. Since 2019, we have also assisted our customers in verifying their performance in k8s environments by preparing and encouraging them to pass the CKA and CKAD exams.

Course Format

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

This instructor-led live training, available online or onsite, targets engineers who aim to deploy and manage software as containers using Docker 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.

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

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

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

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

After completing this training, participants will be capable of:

• Containerizing ML workloads for training, validation, and inference.
• Designing and orchestrating end-to-end ML pipelines with Docker and related tools.
• Implementing versioning, reproducibility, and CI/CD processes for ML components.
• Deploying, monitoring, and scaling 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 for end-to-end containerized workflows.

Course Customization Options

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

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

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

Upon completing this course, participants will be equipped to:

• Create and operate GPU-enabled containers for both training and inference tasks.
• Set up CUDA, drivers, and runtime libraries for containerized AI workflows.
• Optimize resource allocation and isolation for applications that are intensive on GPUs.
• Deploy scalable, containerized deep learning services in production environments.

Format of the Course

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

Course Customization Options

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

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

This instructor-led, live training session (available online or onsite) is designed for advanced professionals looking to architect and deploy distributed AI inference systems within heterogeneous environments.

By the end of 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.
• Optimize inference latency, reliability, and resilience across diverse infrastructure.

Course Format

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

Course 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.

This instructor-led, live training in Slovakia (online or onsite) is aimed at intermediate-level Java developers who wish 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 Slovakia (online or onsite) is designed for intermediate-level developers and DevOps engineers who aim 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 Slovakia (online or onsite) is aimed at developers who wish to transform traditional architecture into a highly concurrent microservices-based architecture using Spring Cloud, Kafka, Docker, Kubernetes and Redis.

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

• Set up the necessary development environment for building microservices.
• Design and implement a highly concurrent microservices ecosystem using Spring Cloud, Kafka, Redis, Docker and Kubernetes.
• Transform monolithic and SOA services to microservice based architecture.
• Adopt a DevOps approach to developing, testing and releasing software.
• Ensure high concurrency among microservices in production.
• Monitor microservices and implement recovery strategies.
• Carry out performance tuning.
• Learn about future trends in microservices architecture.