Building Microservices with Spring Cloud and Docker Training Course

This instructor-led, live training in Mexico (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 the creation of consistent, portable, and reproducible environments specifically designed for AI and machine learning tasks.

This instructor-led live training, available either online or in-person, targets intermediate-level professionals seeking to package their ML codebases, dependencies, and models using Docker to ensure reliable workflows from development to production.

Upon finishing this course, participants will be capable of:

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

Course Format

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

Customization Options

• To tailor this training to your organization's specific needs, please reach out to us to arrange a session.

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

This instructor-led live training, available either online or onsite, targets 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 capable of:

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

• If your organization requires 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).

Kubernetes has emerged as the leading platform for container orchestration.

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

Since 2019, we have also been assisting our clients in validating their Kubernetes skills by preparing them for and encouraging the pursuit of 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 expertise by passing the CKA exam.

Furthermore, the training emphasizes gaining practical experience in Kubernetes Administration. Therefore, we highly recommend participating, even if you do not plan to take the CKA exam.

Course Format

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

Course Customization Options

• To request customized training for this course, please contact us to arrange details.
• 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 organization behind Kubernetes.

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

Additionally, the training emphasizes gaining hands-on experience in Kubernetes application development; therefore, we encourage participation 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 recognized training providers globally in the field of containerization. Since 2019, we have also been helping our customers validate their performance in Kubernetes environments by preparing them to pass the CKA and CKAD exams.

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

This instructor-led live training in Mexico (available online or on-site) is aimed at engineers who want to utilize Docker to deploy and manage software as containers instead of traditional standalone 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 Mexico, 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 Mexico (onsite or remote), participants will learn how to create and manage Docker containers, followed by deploying a sample application inside a container. Participants will also learn how to automate, scale, and manage their containerized applications within a Kubernetes cluster. Furthermore, the training covers 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 live training (available online or on-site) is designed for intermediate to advanced technical professionals seeking to containerize and operationalize complete ML pipelines using Docker.

After completing this training, participants will be able to:

• Containerize workloads for ML training, validation, and inference.
• Design and orchestrate end-to-end ML pipelines leveraging Docker and complementary tools.
• Implement version control, 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 of end-to-end containerized workflows.

Customization Options

• For tailored training aligned with specific ML infrastructure requirements, please reach out to discuss available options.

Course Objectives: Gain both theoretical and practical skills in Docker and Kubernetes.

Utilizing 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-level technical professionals looking to configure, optimize, and run GPU-enabled AI workloads within Docker containers.

Upon completing this course, participants will be equipped to:

• Create and operate GPU-enabled containers for training and inference.
• Configure CUDA, drivers, and runtime libraries for AI workflows in containers.
• Optimize resource allocation and isolation for applications that heavily utilize GPUs.
• Deploy scalable, containerized deep learning services in production settings.

Course Format

• Interactive instruction reinforced with real-world demonstrations.
• Practice exercises focused on developing with GPU support.
• Hands-on implementation within a live laboratory environment.

Course Customization Options

• For customized training that aligns with your infrastructure or GPU stack, please contact us to make arrangements.

Hybrid AI deployment involves executing AI inference across cloud, on-premises, and edge environments through unified, container-based workflows.

This instructor-led live training (available online or onsite) targets advanced professionals seeking to design and deploy distributed AI inference systems within heterogeneous environments.

Upon completing this training, participants will be able to:

• Develop secure and scalable containerized AI services for multi-location settings.
• 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 and expert-led discussions.
• Extensive hands-on practice and applied exercises.
• Real-world experimentation in a controlled live-lab environment.

Customization Options

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

This instructor-led live training in Mexico (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 Mexico (online or onsite) targets intermediate developers and DevOps engineers looking 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 Mexico (online or onsite) targets developers who wish to transition traditional architectures into highly concurrent, microservices-based systems using Spring Cloud, Kafka, Docker, Kubernetes, and Redis.

Upon completing this training, participants will be able to:

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