Building Microservices with Spring Cloud and Docker Training Course

This guided, live training in Argentina (online or at your location) is designed for engineers aiming to expand their Docker knowledge to deploy applications at scale while maintaining control.

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

• Create custom Docker images.
• Deploy and manage a large volume of Docker applications.
• Evaluate various container orchestration solutions to select the most appropriate option.
• Implement continuous integration workflows for Docker applications.
• Integrate Docker applications with existing continuous integration tools.
• Enhance the security of Docker applications.

Docker serves as a containerization platform, providing consistent, portable, and reproducible environments specifically designed for AI and machine learning workloads.

This instructor-led live training, available online or onsite, 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 equipped to:

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

Course Format

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

Course Customization Options

• To tailor this training to your organizational needs, please contact us to make arrangements.

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

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

Upon completion of the 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.
• 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 conducted in 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) program was established by The Linux Foundation and the Cloud Native Computing Foundation (CNCF).

Today, Kubernetes stands as 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 become one of the most recognized training providers globally in the field of containerization.

Since 2019, we have also assisted our clients in validating their skills in Kubernetes environments by preparing them to pass the CKA and CKAD exams.

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

Additionally, the course emphasizes gaining practical experience in Kubernetes application development. We recommend participating even if you do not plan to sit for the CKAD exam.

NobleProg has been providing 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 assisted 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.
• Hands-on implementation in a live laboratory environment.

Course Customization Options

• To request customized training for this course, please contact us to make arrangements.
• For more information about CKAD, please visit: https://training.linuxfoundation.org/certification/certified-kubernetes-application-developer-ckad/

This instructor-led, live training in Argentina (online or onsite) is targeted at engineers who want to leverage Docker to deploy and manage software as containers rather than as traditional standalone applications.

Upon completing this training, participants will be capable of:

• Installing and configuring Docker.
• Comprehending and implementing software containerization.
• Managing applications built on Docker.
• Networking various Docker applications and systems.
• Understanding and editing Docker registries.

Learn how to build, deploy, and administer containerized environments using Docker, Kubernetes, and OpenShift. This five-day, hands-on training covers container images, Kubernetes workloads, cluster networking, storage, security, monitoring, and practical OpenShift administration. Participants will acquire the skills necessary to operate modern container platforms and troubleshoot applications across both development and production environments.

In this instructor-led, live training in Argentina (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 either online or on-site—is tailored for intermediate to advanced technical professionals aiming to containerize and operationalize end-to-end ML pipelines using Docker.

Upon completing this training, participants will be equipped to:

• Containerize workloads related to ML training, validation, and inference.
• Design and orchestrate complete ML pipelines leveraging Docker and complementary tools.
• Establish versioning, reproducibility, and CI/CD practices for ML components.
• Deploy, monitor, and scale ML services within containerized environments.

Course Format

• Interactive lectures complemented 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 programs customized to meet specific ML infrastructure requirements, please reach out to us to explore available options.

Training objectives: Acquire theoretical and operational skills in Docker and Kubernetes.

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

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

Upon completing this course, participants will be able to:

• Build and execute 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 supplemented by real-world demonstrations.
• Exercise-driven practice focused on GPU-enabled development.
• Hands-on implementation in a live-lab environment.

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 session, available online or onsite, targets advanced-level professionals seeking to design and deploy distributed AI inference systems across heterogeneous environments.

Upon completing this training, participants will be equipped to:

• Construct secure and scalable containerized AI services for multi-location infrastructures.
• Deploy AI inference workloads to cloud platforms, local servers, and edge devices using Docker.
• Integrate orchestration tools to automate distributed AI operations.
• Optimize 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 Customization Options

• For tailored adjustments to align this course with your organization’s infrastructure or use cases, please contact us to customize the training.

This instructor-led, live training in Argentina (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.

In this instructor-led, live training in Argentina (onsite or remote), participants will learn how to deploy a collection of sample servers inside containers, then automate, scale, and manage their containerized servers within a Kubernetes cluster. The training goes on to more advanced topics, walking participants through the process of securing, networking and monitoring a Kubernetes cluster.

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

• Set up and run a Docker container.
• Deploy containerized databases and servers.
• Set up a Docker and Kubernetes cluster.
• Use Kubernetes to deploy and manage different environments under the same cluster.
• Secure, scale and monitor a Kubernetes cluster.

This instructor-led, live training in Argentina (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.