Docker for MLOps: End-to-End Pipeline Containerization 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.

This instructor-led, live training in Argentina (online or onsite) is designed for advanced AI engineers and data scientists with intermediate-to-advanced experience who aim to improve DeepSeek model performance, reduce latency, and deploy AI solutions efficiently using contemporary MLOps practices.

Upon completing this training, participants will be able to:

• Optimize DeepSeek models for efficiency, accuracy, and scalability.
• Implement best practices for MLOps and model versioning.
• Deploy DeepSeek models on cloud and on-premise infrastructure.
• Monitor, maintain, and scale AI solutions effectively.

Docker serves as a leading containerization platform, facilitating consistent, portable, and reproducible environments specifically for AI and machine learning operations.

This instructor-led live training, available online or onsite, is designed for intermediate-level professionals aiming to package ML codebases, dependencies, and models using Docker to ensure reliable workflows from development through to production.

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

• Construct and maintain Docker images optimized for AI and ML applications.
• Encapsulate machine learning pipelines, tools, and dependencies within containers.
• Refine Docker environments to enhance performance and ease of portability.
• Implement containerized ML services across diverse runtime environments.

Course Format

• Demonstrations of key concepts paired with guided discussions.
• Practical exercises centered on real-world containerization challenges.
• Application of concepts using live-lab Docker environments.

Customization Opportunities

• To tailor this training to your specific organizational needs, please reach out to us for arrangements.

CI/CD for AI is a structured approach to automating model packaging, testing, containerization, and deployment using continuous integration and continuous delivery pipelines.

This instructor-led, live training (online or onsite) is aimed at intermediate-level professionals who wish to automate end-to-end AI model delivery workflows using Docker and CI/CD platforms.

As the training concludes, participants will be able to:

• Create automated pipelines for building and testing AI model containers.
• Implement version control and reproducibility for model lifecycles.
• Integrate automated deployment strategies for AI services.
• Apply CI/CD best practices tailored to machine learning operations.

Format of the Course

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

Course Customization Options

• If your organization requires customized pipeline workflows or platform integrations, please contact us to tailor this course.

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/

Docker is an open-source platform designed to automate the building, shipping, and running of applications within containers.

This instructor-led live training, available online or on-site, targets engineers looking to adopt Docker for deploying and managing software as containers rather than traditional standalone applications.

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

• Install and configure Docker.
• Comprehend and apply software containerization principles.
• Manage Docker-based applications effectively.
• Connect various Docker applications and systems via networking.
• Understand and manage Docker registries.

Course Format

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

Customization Options

• To request a customized training session for this course, please contact us to arrange details.
• For further information about Docker, visit: https://www.docker.com

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.

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

To execute high-performance deep learning tasks in a scalable and efficient way, GPU acceleration is a critical requirement.

This live, instructor-led training (available online or on-site) is designed for intermediate-level technical professionals looking to configure, optimize, and deploy GPU-enabled AI workloads within Docker containers.

By the end of this course, participants will have the capability to:

• Create and operate GPU-enabled containers for both training and inference processes.
• Set up CUDA, drivers, and runtime libraries to support containerized AI pipelines.
• Enhance resource allocation and ensure isolation for GPU-intensive applications.
• Implement scalable, containerized deep learning services in production settings.

Course Format

• Engaging instruction complemented by real-world examples.
• Practical exercises centered on GPU-enabled development.
• Live-lab hands-on implementation opportunities.

Customization Options

• To align training with your specific infrastructure or GPU stack, please reach out to us for a tailored arrangement.

Kubeflow is an open-source platform designed to streamline building, training, and deploying machine learning workloads on Kubernetes.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level professionals who wish to build reliable ML workflows using Kubeflow.

Upon completion of this training, attendees will gain the skills to:

• Navigate the Kubeflow ecosystem and core components.
• Build reproducible workflows with Kubeflow Pipelines.
• Run scalable training jobs on Kubernetes.
• Serve machine learning models efficiently using Kubeflow Serving.

Format of the Course

• Guided presentations and collaborative discussions.
• Hands-on labs with real Kubeflow components.
• Practical exercises to build end-to-end ML workflows.

Course Customization Options

• Customized versions of this training can be arranged to align with your team’s technology stack and project requirements.

This instructor-led live training, offered in Argentina (either online or onsite), caters to developers and data scientists aiming to build, deploy, and manage machine learning workflows on Kubernetes.

By the end of this program, participants will be capable of:

• Installing and configuring Kubeflow in both on-premise and cloud settings.
• Creating, deploying, and managing ML workflows using Docker containers and Kubernetes.
• Executing full machine learning pipelines across diverse architectures and cloud environments.
• Using Kubeflow to spawn and oversee Jupyter notebooks.
• Building ML training, hyperparameter tuning, and serving workloads on multiple platforms.

This live, instructor-led training in Argentina (online or onsite) is aimed at engineers who wish to evaluate the approaches and tools available today to make an intelligent decision on the path forward in adopting MLOps within their organization.

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

• Install and configure various MLOps frameworks and tools.
• Assemble the right kind of team with the right skills for constructing and supporting an MLOps system.
• Prepare, validate and version data for use by ML models.
• Understand the components of an ML Pipeline and the tools needed to build one.
• Experiment with different machine learning frameworks and servers for deploying to production.
• Operationalize the entire Machine Learning process so that it's reproduceable and maintainable.

MLOps on Kubernetes provides a framework for automating the training, validation, packaging, and deployment of machine learning models through containerized pipelines and GitOps workflows.

This instructor-led, live training (available online or onsite) targets intermediate-level practitioners who aim to construct automated and scalable MLOps pipelines on Kubernetes.

Upon completing this training, participants will be able to:

• Design end-to-end CI/CD pipelines specifically for machine learning.
• Implement GitOps workflows for model deployment and versioning.
• Automate the training, testing, and packaging processes of ML models.
• Integrate monitoring, alerting, and rollback strategies.

Format of the Course

• Instructor-guided presentations and technical deep dives.
• Hands-on exercises that build real-world CI/CD workflows.
• Live-lab practice deploying ML workloads to Kubernetes.

Course Customization Options

• Organizations may request tailored content aligned with their internal MLOps tools and infrastructure.