Building Microservices with Spring Cloud and Docker Training Course

This instructor-led, live training in Australia (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 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 containerisation 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-level professionals looking to package ML codebases, dependencies, and models using Docker to ensure reliable workflows from development to production.

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

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

Format of the Course

• 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 represents a structured methodology for automating the packaging, testing, containerisation, and deployment of models through continuous integration and delivery pipelines.

This instructor-led live training (available online or onsite) is designed for intermediate-level professionals seeking to automate end-to-end AI model delivery workflows using Docker and CI/CD platforms.

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

• Establish automated pipelines for building and testing AI model containers.
• Implement version control and reproducibility measures for model lifecycles.
• Integrate automated deployment strategies for AI services.
• Apply CI/CD best practices 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 organisation requires tailored pipeline workflows or platform integrations, please contact us to customise this course.

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

Kubernetes has now become a leading platform for container orchestration.

NobleProg has been delivering Docker and Kubernetes training since 2015. With over 360 successfully completed training projects, we have established ourselves as one of the most well-known training providers globally in the field of containerisation.

Since 2019, we have also been assisting our customers in validating their performance in Kubernetes environments by preparing them and encouraging them to sit for the CKA and CKAD examinations.

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.

Furthermore, 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.

Format of the Course

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

Course Customization Options

• To request a customized training course, please contact us to make arrangements.
• To learn more about CKA certification, please visit: https://training.linuxfoundation.org/certification/certified-kubernetes-administrator-cka

The Certified Kubernetes Application Developer (CKAD) programme was developed by The Linux Foundation and the Cloud Native Computing Foundation (CNCF), the stewards of Kubernetes.

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

Furthermore, the training emphasises gaining practical experience in Kubernetes application development. We recommend attending, even if you do not currently plan to sit for the CKAD exam.

NobleProg has been delivering Docker and Kubernetes training since 2015. With over 360 successfully completed training projects, we have become one of the most recognised training providers globally in the field of containerisation. Since 2019, we have also been assisting our customers in demonstrating their proficiency in Kubernetes environments by preparing them and encouraging them to pass the CKA and CKAD exams.

Course Format

• Interactive lectures and discussions.
• Ample exercises and practice sessions.
• Hands-on implementation within a live lab environment.

Course Customisation Options

• To request bespoke 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 Australia (online or onsite) is designed for engineers who wish to utilise 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 edit Docker registries.

In this instructor-led, live training in Australia, participants will learn how to manage Red Hat OpenShift Container Platform.

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

• Create, configure, manage, and troubleshoot OpenShift clusters.
• Deploy containerised applications on-premise, in public cloud environments, or on hosted cloud services.
• Secure OpenShift Container Platform
• Monitor systems and gather metrics.
• Manage storage solutions.

In this instructor-led, live training in Australia (onsite or remote), participants will learn how to create and manage Docker containers, before deploying a sample application inside a container. Participants will also discover how to automate, scale, and manage their containerised applications within a Kubernetes cluster. Finally, the training covers more advanced topics, guiding 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 utilised to construct reproducible, portable, and scalable environments for machine learning systems.

This instructor-led, live training (available online or onsite) targets intermediate to advanced-level technical professionals looking 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 within 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: 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 seeking to configure, optimize, and run 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 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.

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 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, live training (available online or onsite) is designed for advanced-level professionals looking to design and deploy distributed AI inference systems across heterogeneous environments.

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

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

Format of the Course

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

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 Australia (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 Australia (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 in Australia (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.