Building Microservices with Spring Cloud and Docker Training Course

This instructor-led live training in Czech Republic (online or on-site) is designed for engineers who want to advance their Docker knowledge 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 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-level professionals seeking to package ML codebases, dependencies, and models using Docker to ensure reliable workflows from development to production.

Upon completing this course, participants will be equipped to:

• Construct and manage Docker images specifically designed for AI and ML applications.
• Containerize machine learning pipelines, tools, and associated dependencies.
• Enhance Docker environments for optimal performance and portability.
• Deploy containerized ML services across diverse runtime environments.

Course Format

• Conceptual demonstrations supported by guided discussions.
• Hands-on exercises focused on real-world containerization tasks.
• Practical implementation using live-lab Docker environments.

Course Customization Options

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

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 training, available online or onsite, is designed for intermediate professionals seeking to automate end-to-end AI model delivery workflows using Docker and CI/CD platforms.

Upon completing the training, participants will be capable of:

• Establishing automated pipelines for constructing and testing AI model containers.
• Enforcing version control and reproducibility throughout model lifecycles.
• Integrating automated deployment strategies for AI services.
• Applying 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

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

The Certified Kubernetes Administrator (CKA) credential was established by The Linux Foundation and the Cloud Native Computing Foundation (CNCF).

Kubernetes has become the dominant platform for container orchestration.

Since 2015, NobleProg has specialized in delivering Docker and Kubernetes training. With over 360 successfully completed training projects, we have established ourselves as one of the world’s leading training providers in the field of containerization.

Since 2019, we have also assisted our clients in validating their expertise in Kubernetes environments by preparing them to pass the CKA and CKAD exams.

This instructor-led live training (available online or onsite) is designed for System Administrators and Kubernetes practitioners who wish to validate their knowledge by passing the CKA exam.

Additionally, the course focuses on gaining practical experience in Kubernetes administration. Therefore, we recommend participating even if you do not plan to take the CKA exam.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practice sessions.
• Hands-on implementation 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) credential has been established by The Linux Foundation and the Cloud Native Computing Foundation (CNCF), which hosts Kubernetes.

This instructor-led live training (available online or onsite) is designed for Developers who want to validate their ability to design, build, configure, and expose cloud-native applications for Kubernetes.

Furthermore, the training emphasizes gaining practical experience in Kubernetes application development, so we recommend attending 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 world's most recognized training companies in the field of containerization. Since 2019, we have also assisted our customers in verifying their performance in k8s environments by preparing them and encouraging them to pass the CKA and CKAD exams.

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 CKAD, please visit: https://training.linuxfoundation.org/certification/certified-kubernetes-application-developer-ckad/

This instructor-led, live training in Czech Republic (online or onsite) is designed for engineers who want to leverage Docker to deploy and manage software as containers rather than as traditional standalone applications.

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

• Install and configure Docker.
• Understand and implement software containerization.
• Manage Docker-based applications.
• Connect various Docker applications and systems via networking.
• Understand and modify Docker registries.

In this instructor-led, live training in Czech Republic, 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 Czech Republic (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 live training, available online or on-site, targets technical professionals at intermediate to advanced levels who aim to containerize and operationalize full machine learning pipelines using Docker.

Upon completing this training, participants will be equipped to:

• Containerize ML workloads covering training, validation, and inference.
• 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 complemented by practical demonstrations.
• Hands-on exercises focused on constructing real-world ML pipeline components.
• Live-lab sessions for implementing end-to-end containerized workflows.

Customization Options

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

Course Objectives: Acquire theoretical and practical skills in Docker and Kubernetes.

GPU acceleration is crucial for executing high-performance deep learning workloads in a scalable and efficient way.

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

Upon completing this course, participants will be able to:

• Build and run GPU-enabled containers for both training and inference tasks.
• 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.

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-premise, and edge environments using unified container-based workflows.

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

Upon completing this training, participants will be equipped to:

• Construct secure and scalable containerized AI services for multi-location setups.
• Deploy AI inference workloads to cloud platforms, local servers, and edge devices using Docker.
• Leverage 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 with applied exercises.
• Real-world experimentation within a controlled live-lab environment.

Customization Options

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

This instructor-led, live training in Czech Republic (online or onsite) is designed for intermediate-level Java developers aiming to design, develop, deploy, and maintain microservices-based applications utilizing Java frameworks such as Spring Boot and Spring Cloud.

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

• Grasping the core principles and advantages of microservices architecture.
• Constructing and deploying microservices using Java and Spring Boot.
• Implementing service discovery, configuration management, and API gateways.
• Effectively securing, monitoring, and scaling microservices.
• Deploying microservices using Docker and Kubernetes.

This instructor-led, live training in Czech Republic (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 Czech Republic (online or onsite) targets developers aiming to transition traditional architectures into highly concurrent microservices-based systems utilizing Spring Cloud, Kafka, Docker, Kubernetes, and Redis.

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

• Configure the required development environment for microservice construction.
• Design and deploy a highly concurrent microservices ecosystem leveraging Spring Cloud, Kafka, Redis, Docker, and Kubernetes.
• Migrate monolithic and SOA services into a microservice-oriented architecture.
• Implement DevOps practices for software development, testing, and release.
• Guarantee high concurrency levels for microservices in production environments.
• Monitor microservices and establish recovery protocols.
• Perform performance optimization.
• Explore emerging trends in microservices architecture.