Building Microservices with Spring Cloud and Docker Training Course

This instructor-led, live training in Greece (online or on-site) is designed for engineers who wish to advance their knowledge of Docker 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.

Upon completion, participants will be able to:

• Create custom Docker images.
• Deploy and manage a high volume of Docker applications.
• Evaluate various container orchestration solutions and select the most appropriate option.
• Establish a continuous integration workflow for Docker applications.
• Integrate Docker applications with existing continuous integration toolchains.
• Implement security measures for Docker applications.
• Leverage Kubernetes to deploy and manage diverse environments within a single cluster.
• Secure, scale, and monitor a Kubernetes cluster effectively.

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

This instructor-led live training, available online or onsite, is designed for intermediate 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:

• Create and manage Docker images specifically tailored for AI and ML applications.
• Containerize machine learning pipelines, tools, and their associated dependencies.
• Optimize Docker environments to enhance performance and portability.
• Deploy containerized ML services across various runtime environments.

Course Format

• Conceptual demonstrations accompanied by guided discussion.
• Practical exercises focusing on real-world containerization challenges.
• Hands-on implementation within live-lab Docker environments.

Customization Options

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

CI/CD for AI represents a structured methodology for automating the packaging, testing, containerization, and deployment of AI models via continuous integration and delivery pipelines.

This instructor-led training, available online or onsite, targets intermediate-level professionals aiming to automate end-to-end AI model delivery workflows using Docker and CI/CD platforms.

Upon completing the training, participants will be equipped to:

• Establish automated pipelines for constructing and testing AI model containers.
• Enforce version control and reproducibility throughout model lifecycles.
• Integrate automated deployment strategies for AI services.
• Apply CI/CD best practices specifically adapted for machine learning operations.

Course Format

• Instructor-led presentations coupled with 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) program was established by The Linux Foundation and the Cloud Native Computing Foundation (CNCF).

Today, Kubernetes stands as a leading platform for container orchestration.

NobleProg has been providing Docker and Kubernetes training since 2015. Having successfully completed over 360 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 within 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 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.
• Ample exercises and practice opportunities.
• Hands-on implementation in a live laboratory environment.

Course Customization Options

• To request customized training for this course, please contact us to arrange details.
• 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, training is also focused on gaining practical experience in Kubernetes application development, so we recommend taking part in it, even if you don't intend to take CKAD exam.

NobleProg have been delivering Docker & Kubernetes training from 2015. With more than 360 successfully completed training projects, we became one of the best known training company worldwide in field of containerization. Since 2019 we are also helping our customers to confirm their performance in k8s environment by preparing them and encouraging to pass CKA and CKAD exams.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a 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 Greece (online or onsite) is tailored for engineers who want to deploy and manage software as containers using Docker, rather than as conventional standalone applications.

By the conclusion 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, 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 containerised 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 Greece (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 construct reproducible, portable, and scalable environments for machine learning systems.

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

By the end of this training, participants will be capable of:

• Containerizing ML training, validation, and inference workloads.
• Designing and orchestrating end-to-end ML pipelines using Docker and complementary tools.
• Implementing versioning, reproducibility, and CI/CD practices for ML components.
• Deploying, monitoring, and scaling ML services within containerized environments.

Course Format

• Interactive lectures accompanied by practical demonstrations.
• Hands-on exercises centered on building real-world ML pipeline components.
• Live laboratory sessions for implementing end-to-end containerized workflows.

Course Customization Options

Training Objectives: Acquire theoretical and operational skills in Docker and Kubernetes.

Harnessing GPU acceleration is crucial for executing high-performance deep learning workloads efficiently and at scale.

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

Upon completing this course, participants will be capable of:

• Constructing and executing containers equipped with GPU support for both training and inference tasks.
• Setting up CUDA, drivers, and runtime libraries specifically for containerized AI workflows.
• Optimizing resource allocation and isolation for applications that are intensive on GPUs.
• Deploying scalable, containerized deep learning services within production environments.

Course Format

• Interactive instruction complemented by real-world demonstrations.
• Exercise-driven practice centered on GPU-enabled development.
• Hands-on implementation within a live laboratory environment.

Customization Options

• For specialized training tailored to your specific infrastructure or GPU stack, please reach out to us to make arrangements.

This instructor-led, live training in Greece (online or onsite) targets intermediate Java developers who aim to design, develop, deploy, and maintain microservices-based applications using Java frameworks such as Spring Boot and Spring Cloud.

Upon completing this training, participants will be able to:

• Comprehend the core principles and advantages of microservices architecture.
• Develop and deploy microservices utilizing Java and Spring Boot.
• Implement service discovery, configuration management, and API gateways.
• Effectively secure, monitor, and scale microservices.
• Deploy microservices leveraging Docker and Kubernetes.

This instructor-led, live training in Greece (online or onsite) 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 instructor-led, live training in Greece (online or onsite) targets developers who want to shift from traditional architecture to a highly concurrent, microservices-based structure using Spring Cloud, Kafka, Docker, Kubernetes, and Redis.

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

• Set up the required 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 into a microservice-based architecture.
• Adopt a DevOps approach to software development, testing, and release.
• Ensure high concurrency among microservices in production.
• Monitor microservices and implement recovery strategies.
• Perform performance tuning.
• Learn about future trends in microservices architecture.