Building Microservices with Spring Cloud and Docker Training Course

This instructor-led, live training in Italy (online or onsite) is designed for engineers who wish to deepen their Docker knowledge to deploy applications at scale while maintaining control.

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

• Creating custom Docker images.
• Deploying and managing a high volume of Docker applications.
• Evaluating various container orchestration solutions and selecting the most appropriate one.
• Establishing a continuous integration workflow for Docker applications.
• Integrating Docker applications into existing continuous tooling processes.
• Ensuring the security of Docker applications.

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

• Create custom Docker images.
• Deploy and manage a high volume of Docker applications.
• Assess various container orchestration solutions and select the most appropriate one.
• Establish a continuous integration workflow for Docker applications.
• Integrate Docker applications into existing continuous integration toolchains.
• Enhance the security of Docker applications.
• Utilize Kubernetes to deploy and manage diverse environments within a single cluster.
• Secure, scale, and monitor a Kubernetes cluster.

Docker serves as a containerization platform that ensures consistent, portable, and reproducible environments for AI and machine learning tasks.

This live, instructor-led training, available online or onsite, targets intermediate professionals seeking to package ML codebases, dependencies, and models using Docker for reliable workflows from development to production.

Upon completing this course, participants will be capable of:

• Creating and managing Docker images tailored for AI and ML applications.
• Containerizing machine learning pipelines, tools, and dependencies.
• Optimizing Docker environments for both performance and portability.
• Deploying containerized ML services across various runtime environments.

Course Format

• Concept demonstrations reinforced by guided discussions.
• Hands-on exercises centered on real-world containerization challenges.
• Practical implementation within live-lab Docker environments.

Customization Options

• To tailor this training to your organizational context, please reach out to us to make arrangements.

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 professionals looking to automate end-to-end AI model delivery workflows by leveraging Docker and CI/CD platforms.

Upon completing the training, participants will be equipped to:

• Design automated pipelines for constructing and testing AI model containers.
• Establish version control and reproducibility standards throughout the model lifecycle.
• Incorporate automated deployment strategies for AI services.
• Apply CI/CD best practices specifically adapted for machine learning operations.

Course Format

• Instructor-guided presentations and technical discussions.
• Practical labs and hands-on implementation exercises.
• Realistic CI/CD workflow simulations 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.

Since 2015, NobleProg has been providing Docker and Kubernetes training. With over 360 successfully completed training projects, we have established ourselves as one of the most recognized training providers globally in the field of containerization.

Since 2019, we have also been supporting our customers in validating their skills in k8s 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.

Moreover, the training focuses on gaining practical experience in Kubernetes Administration, so we recommend participating even if you do not plan to take the CKA exam.

Course Format

• Interactive lectures and discussions.
• Abundant 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 arrange.
• To learn more about CKA certification, please visit: https://training.linuxfoundation.org/certification/certified-kubernetes-administrator-cka

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

This instructor-led, live training (available online or in-person) is designed for developers looking to validate their ability to design, build, configure, and expose cloud-native applications on Kubernetes.

Additionally, 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 and 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 Kubernetes environments by preparing them to pass the CKA and CKAD exams.

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 arrange.
• To learn more about CKAD, please visit: https://training.linuxfoundation.org/certification/certified-kubernetes-application-developer-ckad/

This instructor-led, live training in Italy (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.

In this instructor-led, live training in Italy, 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 containerized 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 Italy (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 create reproducible, portable, and scalable environments for machine learning systems.

This instructor-led live training, available both online and onsite, targets intermediate to advanced technical professionals seeking to containerize and operationalize complete ML pipelines using Docker.

Upon completing this training, participants will be able to:

• Containerize 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 containerized environments.

Course Format

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

Customization Options

• For training tailored to specific ML infrastructure requirements, please contact us to explore 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 who wish to configure, optimize, and run GPU-enabled AI workloads inside 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 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 by leveraging unified, container-based workflows.

This instructor-led training session, available online or onsite, is designed for advanced-level professionals seeking to design and deploy 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 deployments.
• Deploy AI inference workloads to cloud platforms, local servers, and edge devices using Docker.
• Integrate orchestration tools to automate distributed AI operations.
• Enhance inference latency, reliability, and resilience across diverse infrastructure.

Course Format

• Guided presentations complemented by expert-led discussions.
• Extensive hands-on practice and 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 Italy (online or onsite) is designed for intermediate-level Java developers seeking to design, develop, deploy, and maintain microservices-based applications using Java frameworks like Spring Boot and Spring Cloud.

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

• Comprehend 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 Italy (online or onsite) targets intermediate-level developers and DevOps engineers who wish to construct, 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.