Building Microservices with Spring Cloud and Docker Training Course

This instructor-led, live training in France (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 facilitates consistent, portable, and reproducible environments for AI and machine learning workloads.

This instructor-led live training, available either online or on-site, targets intermediate-level professionals aiming 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 dependencies.
• Optimize Docker environments for both performance and portability.
• Deploy containerized ML services across various runtime environments.

Course Format

• Conceptual demonstrations accompanied by guided discussions.
• Hands-on exercises centered on real-world containerization challenges.
• Practical implementation using live-lab Docker environments.

Course Customization Options

• To tailor this training to your organizational needs, please contact us to arrange a session.

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

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

Upon completion of the training, participants will be capable of:

• Establishing automated pipelines for constructing and testing AI model containers.
• Implementing version control and ensuring reproducibility throughout the model lifecycle.
• Integrating automated deployment strategies for AI services.
• Applying 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 accordingly.

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

Kubernetes has become a leading platform for container orchestration.

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

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

This instructor-led, live training (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 lectures and discussions.
• 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 CKA certification, please visit: https://training.linuxfoundation.org/certification/certified-kubernetes-administrator-cka

The Certified Kubernetes Application Developer (CKAD) program was created by The Linux Foundation and the Cloud Native Computing Foundation (CNCF), the host organization for Kubernetes.

This instructor-led, live training (available online or onsite) is designed for Developers who want to validate their expertise in designing, building, configuring, and exposing cloud native applications on Kubernetes.

Furthermore, the training emphasizes gaining practical experience in Kubernetes application development; therefore, we recommend participating even if you do not plan to take the CKAD exam.

NobleProg has been providing Docker & Kubernetes training since 2015. With over 360 successfully completed training projects, we have become one of the most recognized training providers worldwide in the field of containerization. Since 2019, we have also been assisting our customers in validating their performance in k8s environments by preparing them and encouraging them to pass the CKA and CKAD exams.

Course Format

• Interactive lectures and discussions.
• Abundant 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 make arrangements.
• To learn more about CKAD, please visit: https://training.linuxfoundation.org/certification/certified-kubernetes-application-developer-ckad/

This instructor-led, live training in France (online or onsite) is designed for engineers who wish to use Docker to deploy and manage software as containers instead of as traditional stand-alone software.

By the end 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.

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

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

• Creating, configuring, managing, and troubleshooting OpenShift clusters.
• Deploying containerized applications within on-premise environments, public clouds, or hosted clouds.
• Implementing security measures for OpenShift Container Platform
• Monitoring systems and collecting metrics.
• Managing storage resources.

In this instructor-led live training in France (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 training session, available either online or onsite, targets technical professionals with intermediate to advanced skills who aim to containerize and operationalize complete ML pipelines using Docker.

After completing this training, participants will be equipped to:

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

Course Format

• Interactive lectures accompanied by practical demonstrations.
• Hands-on exercises centered on constructing real-world ML pipeline components.
• Live-lab implementation focused on end-to-end containerized workflows.

Course Customization Options

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

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

GPU acceleration is critical for executing high-performance deep learning tasks in a scalable and efficient way.

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

Upon completing this course, participants will be able to:

• Create and operate GPU-enabled containers for both training and inference.
• Set up 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 through unified, container-based workflows.

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

After completing this training, participants will be capable of:

• Developing secure and scalable containerized AI services for multi-site environments.
• Deploying AI inference workloads to cloud platforms, local servers, and edge devices using Docker.
• Integrating orchestration tools to automate distributed AI operations.
• Enhancing inference latency, reliability, and resilience across diverse infrastructures.

Course Format

• Guided presentations and discussions led by experts.
• Ample hands-on practice and applied exercises.
• Real-world experimentation within a controlled live-lab environment.

Customization Options

• For personalized adjustments to align this course with your organization's infrastructure or specific use cases, please contact us to arrange custom training.

This instructor-led, live training in France (offered online or on-site) is designed for intermediate Java developers who want 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 France (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 France (online or onsite) is designed for developers who wish to transform traditional architectures into highly concurrent microservices-based systems using Spring Cloud, Kafka, Docker, Kubernetes, and Redis.

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

• Configure the necessary development environment for building microservices.
• Design and implement a highly concurrent microservices ecosystem using Spring Cloud, Kafka, Redis, Docker, and Kubernetes.
• Migrate monolithic and SOA services to a microservice-based architecture.
• Adopt a DevOps approach for software development, testing, and release.
• Ensure high concurrency among microservices in production environments.
• Monitor microservices and implement effective recovery strategies.
• Perform performance tuning.
• Gain insights into future trends in microservices architecture.