Building Microservices with Spring Cloud and Docker Training Course

This instructor-led, live training in Taiwan (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:

• Build their own Docker images.
• Deploy and manager 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 designed to establish consistent, portable, and reproducible environments for artificial intelligence and machine learning applications.

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 workflows from development to production.

Upon completing this course, participants will be able to:

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

Course Format

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

Customization Options

• To tailor this training to your organization's specific needs, please contact us to make arrangements.

CI/CD for AI represents a systematic methodology for automating the packaging, testing, containerization, and deployment of machine learning models through continuous integration and continuous delivery pipelines.

This instructor-led live training, available both online and 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 able to:

• Establish automated pipelines for building and testing AI model containers.
• Implement version control and ensure reproducibility throughout model lifecycles.
• Integrate automated deployment strategies for AI services.
• Apply CI/CD best practices specifically tailored 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

• For organizations requiring customized pipeline workflows or specific platform integrations, please contact us to tailor this course to your needs.

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

Kubernetes has emerged as a leading platform for container orchestration.

NobleProg has been delivering Docker & Kubernetes training since 2015. With over 360 successfully completed training projects, we have become one of the most renowned training providers globally in the field of containerization.

Since 2019, we have also been assisting our customers in validating their performance in k8s environments by preparing them for and encouraging 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, so we recommend participating even if you do not intend to take the CKA exam.

Course Format

• Interactive lecture and discussion.
• 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) credential was established by The Linux Foundation and the Cloud Native Computing Foundation (CNCF), the governing body for 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 on Kubernetes.

Additionally, the training emphasizes practical experience in Kubernetes application development, so we highly recommend attending even if you do not plan to take the CKAD exam.

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 helped our customers validate their performance in Kubernetes environments by preparing and encouraging them to pass the CKA and CKAD exams.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practice.
• Hands-on implementation in a live laboratory 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 Taiwan (online or on-site) is targeted at engineers who want to adopt Docker for deploying and managing 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.
• Network different Docker applications and systems.
• Understand and edit Docker registries.

Learn how to build, deploy, and administer containerized environments using Docker, Kubernetes, and OpenShift. This five-day, hands-on training covers container images, Kubernetes workloads, cluster networking, storage, security, monitoring, and practical OpenShift administration. Participants gain the skills needed to operate modern container platforms and troubleshoot applications across development and production environments.

Through this instructor-led, live training in Taiwan (onsite or remote), participants will learn to create and manage Docker containers and deploy a sample application within one. Attendees will also gain skills in automating, scaling, and managing containerized applications using a Kubernetes cluster. The training concludes with advanced topics, guiding participants through the processes of securing, scaling, and monitoring a Kubernetes cluster.

Upon completing this training, participants will be able to:

• Set up and operate a Docker container.
• Deploy a containerized server and web application.
• Build and manage Docker images.
• Configure 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 build reproducible, portable, and scalable environments for machine learning systems.

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

Upon completion of 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 supporting tools.
• Implement versioning, reproducibility, and CI/CD for ML components.
• Deploy, monitor, and scale ML services in containerized environments.

Course Format

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

Course Customization Options

• For customized training aligned with specific ML infrastructure needs, please contact us to discuss options.

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

GPU acceleration is critical 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 want to configure, optimize, and deploy GPU-enabled AI workloads within Docker containers.

Upon completion 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.

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 through unified container-based workflows.

This instructor-led live training, available online or onsite, is designed for advanced professionals seeking to design and deploy distributed AI inference systems across heterogeneous environments.

Upon completing this training, participants will be able to:

• Construct secure and scalable containerized AI services for multi-location settings.
• 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 environment.

Customization Options

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

This instructor-led, live training in Taiwan (online or onsite) is designed for intermediate-level Java developers who aspire 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 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 utilizing Docker and Kubernetes.

In this instructor-led live training at Taiwan (onsite or remote), participants will learn how to deploy sample servers within containers and subsequently automate, scale, and manage these containerized servers inside a Kubernetes cluster. The training advances to more complex subjects, walking participants through the steps to secure, network, and monitor 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.

This instructor-led live training in Taiwan (online or onsite) targets intermediate-level developers and DevOps engineers aiming to build, deploy, and manage microservices using Spring Cloud and Docker.

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

• Develop microservices utilizing Spring Boot and Spring Cloud.
• Containerize applications using Docker and Docker Compose.
• Implement service discovery, API gateways, and inter-service communication.
• Monitor and secure microservices within production environments.
• Deploy and orchestrate microservices using Kubernetes.