Building Microservices with Spring Cloud and Docker Training Course

This instructor-led, live training in Malaysia (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 that provides consistent, portable, and reproducible environments for artificial intelligence and machine learning workloads.

This instructor-led live training, available either 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 completing this course, participants will be able to:

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

Course Format

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

Customization Options

• To tailor this training to your organization's specific environment, please contact us to arrange.

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

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

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

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

Course Format

• Instructor-guided presentations coupled with technical discussions.
• Practical labs and hands-on implementation exercises.
• Realistic CI/CD workflow simulations conducted within a controlled environment.

Course Customization Options

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

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

Kubernetes has emerged as the premier 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 leading training providers globally in the field of containerization.

Since 2019, we have also supported our clients in validating their proficiency in Kubernetes environments by preparing them and encouraging them to take 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 participation even if you do not intend to sit for 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 make arrangements.
• For more information about CKA certification, please visit: https://training.linuxfoundation.org/certification/certified-kubernetes-administrator-cka

The Certified Kubernetes Application Developer (CKAD) programme 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 wish to validate their skills in designing, building, configuring, and exposing cloud native applications for Kubernetes.

Additionally, the training focuses on gaining practical experience in Kubernetes application development. Therefore, we recommend participating even if you do not intend to take 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 most renowned training companies globally in the field of containerization. Since 2019, we have also assisted our customers in validating their performance in the k8s environment by preparing them and encouraging them to pass the CKA and CKAD exams.

Course Format

• Interactive lecture and discussion.
• 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 Malaysia (online or onsite) is designed for engineers who want to use Docker to deploy and manage software as containers instead of as traditional standalone software.

Upon completing this training, participants will be capable of:

• Installing and configuring Docker.
• Understanding and implementing software containerization.
• Managing Docker-based applications.
• Networking distinct Docker applications and systems.
• Understanding and editing Docker registries.

In this instructor-led live training in Malaysia, 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 Malaysia (onsite or remote), participants will learn how to create and manage Docker containers and 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 progresses to 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 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 training session, available online or on-site, targets intermediate to advanced technical professionals seeking to containerize and operationalise complete ML pipelines using Docker.

Upon completing this training, participants will gain the ability to:

• Containerise ML training, validation, and inference workloads.
• Design and orchestrate end-to-end ML pipelines utilizing Docker and complementary tools.
• Implement versioning, reproducibility, and CI/CD practices for ML components.
• Deploy, monitor, and scale ML services within containerised environments.

Course Format

• Interactive lectures complemented by practical demonstrations.
• Hands-on exercises centred on constructing real-world ML pipeline components.
• Live-lab implementation of end-to-end containerised workflows.

Course Customization Options

• For training tailored to specific ML infrastructure requirements, please contact us to discuss available options.

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

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

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

Upon completing 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.

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 Customisation 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 infrastructures using standardized, container-driven workflows.

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

Upon completing this training, participants will be equipped to:

• Construct secure and scalable containerized AI services for multi-site operations.
• Deploy AI inference workloads across cloud platforms, local servers, and edge devices utilizing Docker.
• Integrate orchestration tools to automate distributed AI operations.
• Enhance inference latency, reliability, and resilience across diverse infrastructure setups.

Course Format

• Guided presentations complemented by expert-led discussions.
• Extensive hands-on practice through 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 to arrange customization.

This instructor-led, live training in Malaysia (online or onsite) is designed for intermediate-level Java developers who want 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:

• Grasp the principles and advantages of microservices architecture.
• Construct and deploy microservices using Java and Spring Boot.
• Implement service discovery, configuration management, and API gateways.
• Effectively secure, monitor, and scale microservices.
• Deploy microservices using Docker and Kubernetes.

This instructor-led, live training in Malaysia (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 Malaysia (online or onsite) is tailored for developers who aim to transition from traditional architecture to a highly concurrent microservices-based architecture by leveraging Spring Cloud, Kafka, Docker, Kubernetes, and Redis.

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

• Establish the necessary development environment for building microservices.
• Design and implement a highly concurrent microservices ecosystem utilizing 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 processes.
• Ensure high concurrency among microservices in production environments.
• Monitor microservices and implement effective recovery strategies.
• Conduct performance tuning.
• Gain insights into future trends in microservices architecture.