Building Microservices with Spring Cloud and Docker Training Course

This instructor-led, live training in New Zealand (available online or on-site) is designed 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 different container orchestration solutions and select the most suitable one.
• Establish a continuous integration pipeline for Docker applications.
• Integrate Docker applications with existing continuous integration tools and processes.
• Secure their Docker applications.

Docker is a containerisation platform that enables consistent, portable, and reproducible environments for AI and machine learning workloads.

This instructor-led, live training (online or onsite) is aimed at intermediate-level professionals who wish to package ML codebases, dependencies, and models using Docker for reliable development-to-production workflows.

Upon completing this course, participants will be able to:

• Build and manage Docker images tailored for AI and ML applications.
• Containerise machine learning pipelines, tools, and dependencies.
• Optimise Docker environments for performance and portability.
• Deploy containerised ML services across different runtime environments.

Course Format

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

Course Customisation Options

• To customise this training for your organisational environment, please contact us to arrange.

CI/CD for AI is a structured approach to automating model packaging, testing, containerisation, and deployment using continuous integration and continuous delivery pipelines.

This instructor-led, live training (online or onsite) is aimed at intermediate-level professionals who wish 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:

• Create 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 tailored to machine learning operations.

Course Format

• Instructor-led presentations and technical discussions.
• Practical labs and hands-on implementation exercises.
• Realistic CI/CD workflow simulations in a controlled environment.

Course Customisation Options

• If your organisation requires customised pipeline workflows or platform integrations, please contact us to tailor this course.

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

Kubernetes is now a leading platform used for container orchestration.

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 containerisation.

Since 2019, we have also been helping our customers confirm their performance in k8s environments by preparing them and encouraging them to pass the CKA and CKAD exams.

This instructor-led, live training (online or on-site) is aimed at System Administrators and Kubernetes users who wish to confirm their knowledge by passing the CKA exam.

On the other hand, the training is also focused on gaining practical experience in Kubernetes Administration, so we recommend taking part in it, even if you do not intend to take the CKA exam.

Course Format

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

Course Customisation Options

• To request customised 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) programme 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 designing, building, configuring, and exposing cloud-native applications for Kubernetes.

Furthermore, the training is also focused on gaining practical experience in Kubernetes application development, so we recommend taking part even if you do not intend to sit 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 containerisation. Since 2019, we have also been helping our customers confirm their performance in k8s environments by preparing them and encouraging them to pass CKA and CKAD exams.

Course Format

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

Course Customisation Options

• To request a customised training session 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 New Zealand (available online or on-site) is tailored for engineers who wish to adopt Docker to deploy and manage software as containers, rather than as traditional stand-alone applications.

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

• Install and configure Docker.
• Understand and implement software containerisation.
• Manage Docker-based applications.
• Network different Docker applications and systems.
• Understand and work with Docker registries.

In this instructor-led, live training in New Zealand, 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 Red Hat OpenShift Container Platform
• Monitor and gather metrics.
• Manage storage.

In this instructor-led, live training in New Zealand (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 containerised 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 containerised 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 is a containerisation platform used to build reproducible, portable, and scalable environments for ML systems.

This instructor-led, live training (online or on-site) is designed for intermediate to advanced-level technical professionals who wish to containerise and operationalise complete ML pipelines using Docker.

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

• Containerise 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 containerised environments.

Course Format

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

Course Customisation Options

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

Training objectives: Gain 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 on-site) is designed for intermediate-level technical professionals who wish to configure, optimise, and run GPU-enabled AI workloads inside Docker containers.

By the end 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 containerised AI workflows.
• Optimise resource allocation and isolation for GPU-intensive applications.
• Deploy scalable, containerised 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 Customisation Options

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

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

This instructor-led, live training (available online or on-site) is designed for advanced-level professionals who wish to design and deploy distributed AI inference systems across heterogeneous environments.

Upon completing this training, participants will be able to:

• Build secure and scalable containerised AI services for multi-location environments.
• Deploy AI inference workloads to cloud platforms, local servers, and edge devices using Docker.
• Integrate orchestration tools to automate distributed AI operations.
• Optimise 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.

Course Customisation Options

• For tailored adjustments to align this course with your organisation's infrastructure or use cases, please contact us to customise the training.

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

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

• Understand the core 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 effectively scale microservices.
• Deploy microservices using Docker and Kubernetes.

This instructor-led, live training in New Zealand (online or on-site) is designed for intermediate-level developers and DevOps engineers who aspire to build, deploy, and manage microservices using Spring Cloud and Docker.

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

• Develop microservices leveraging Spring Boot and Spring Cloud.
• Containerise 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.

This instructor-led, live training in New Zealand (available online or on-site) is tailored for developers aiming to transform traditional architectures into highly concurrent, microservices-based systems using Spring Cloud, Kafka, Docker, Kubernetes, and Redis.

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

• Establish the essential development environment required for building microservices.
• Design and implement a highly concurrent microservices ecosystem leveraging Spring Cloud, Kafka, Redis, Docker, and Kubernetes.
• Migrate monolithic and SOA-based services to a microservices architecture.
• Embrace a DevOps approach to software development, testing, and deployment.
• Ensure high concurrency across microservices in production environments.
• Monitor microservices and implement effective recovery strategies.
• Perform performance tuning.
• Explore emerging trends in microservices architecture.