Docker for MLOps: End-to-End Pipeline Containerization Training Course

This instructor-led, live training in Nigeria (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 manage 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.

This instructor-led, live training in Nigeria (online or onsite) is aimed at advanced-level AI engineers and data scientists with intermediate-to-advanced experience who wish to enhance DeepSeek model performance, minimize latency, and deploy AI solutions efficiently using modern MLOps practices.

By the end of 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 robust containerization platform, delivering consistent, portable, and reproducible environments essential for AI and machine learning workloads.

Designed for intermediate-level professionals, this instructor-led live training—available online or onsite—focuses on packaging ML codebases, dependencies, and models within Docker to ensure reliable development-to-production workflows.

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

• Construct and maintain Docker images specifically optimized for AI and ML applications.
• Encapsulate machine learning pipelines, tools, and their associated dependencies within containers.
• Enhance Docker environments for superior performance and ease of portability.
• Roll out containerized ML services across diverse runtime environments.

Course Delivery Format

• Conceptual demonstrations reinforced through guided discussions.
• Practical hands-on exercises centered on real-world containerization challenges.
• Implementation of strategies using live-lab Docker environments.

Customization Options

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

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

This instructor-led live training, available either online or on-site, targets intermediate-level professionals seeking to streamline end-to-end AI model delivery workflows using Docker alongside CI/CD platforms.

Upon completing the training, participants will be equipped to:

• Develop 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-led presentations and technical discussions.
• Practical laboratories and hands-on implementation exercises.
• Realistic simulations of CI/CD workflows within a controlled setting.

Customization Options

• If your organization needs tailored pipeline workflows or specific platform integrations, please reach out to us for course customization.

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

Kubernetes has emerged 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 been assisting our customers in validating their expertise in Kubernetes environments by preparing them to pass 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.

Furthermore, the training focuses on gaining practical experience in Kubernetes Administration; therefore, we recommend participating even if you do not intend to sit for the CKA exam.

Course Format

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

Course Customization Options

• To request a customized training for this course, please contact us to make arrangements.
• 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 developed by The Linux Foundation and the Cloud Native Computing Foundation (CNCF), the host of Kubernetes.

This instructor-led, live training (online or onsite) is aimed at Developers who wish to confirm their skills in design, build, configure, and expose cloud native applications for Kubernetes.

On the other hand, training is also focused on gaining practical experience in Kubernetes application development, so we recommend taking part in it, even if you don't intend to take CKAD exam.

NobleProg have been delivering Docker & Kubernetes training from 2015. With more than 360 successfully completed training projects, we became one of the best known training company worldwide in field of containerization. Since 2019 we are also helping our customers to confirm their performance in k8s environment by preparing them and encouraging to pass CKA and CKAD exams.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a 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/

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

This instructor-led live training, available both online and onsite, targets engineers who want to deploy and manage software as containers rather than as traditional standalone applications.

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.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.
• To learn more about Docker, please visit: https://www.docker.com

Discover 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 necessary skills to operate modern container platforms and troubleshoot applications across both development and production settings.

In this instructor-led, live training in Nigeria (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 practical skills in Docker and Kubernetes.

The "Advanced Docker and Kubernetes" course is a comprehensive training covering the Kubernetes platform and its ecosystem. It caters to participants with diverse experience levels, ensuring a full range of both basic and advanced Kubernetes functionalities. Key concepts covered include Containers, Labels, Controllers, Services, Secrets, Persistent Volumes, Namespaces, Quotas, Container Networking Model, Service Discovery, Scaling, Load Balancing, Cluster Management, Kubernetes Installation, Cluster Security, Access Control, Controller High Availability, Monitoring and Logging, Application Auto-composition, Advanced Scheduling, Microservices-based Applications, Application Design Patterns, and Deploying Applications on a Kubernetes Cluster.

Kubeflow is an open-source platform engineered to simplify the creation, training, and deployment of machine learning workloads on Kubernetes.

This instructor-led live training, available online or onsite, targets beginner to intermediate professionals eager to establish robust ML workflows via Kubeflow.

After completing this training, participants will acquire the competencies to:

• Traverse the Kubeflow ecosystem and its fundamental components.
• Create reproducible workflows utilizing Kubeflow Pipelines.
• Execute scalable training jobs on Kubernetes.
• Efficiently deploy machine learning models using Kubeflow Serving.

Course Format

• Structured presentations alongside collaborative discussions.
• Practical labs involving real Kubeflow components.
• Functional exercises designed to build comprehensive ML workflows.

Course Customization Options

• Tailored versions of this training can be arranged to suit your team’s specific technology stack and project needs.

This instructor-led, live training in Nigeria (delivered online or onsite) is tailored for developers and data scientists looking to build, deploy, and manage machine learning workflows on Kubernetes.

By the end of this program, participants will have the ability to:

• Install and set up Kubeflow in on-premise and cloud environments.
• Create, deploy, and control ML workflows using Docker containers and Kubernetes.
• Execute full machine learning pipelines across different architectures and cloud infrastructures.
• Use Kubeflow to generate and administer Jupyter notebooks.
• Implement ML training, hyperparameter tuning, and serving workloads across various platforms.

This instructor-led live training in Nigeria (online or onsite) is designed for engineers who wish to evaluate the available approaches and tools to make informed decisions on adopting MLOps within their organisations.

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 serves as 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 either online or onsite, is designed for intermediate-level practitioners seeking 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 to manage model deployment and versioning.
• Automate the training, testing, and packaging of ML models.
• Integrate monitoring, alerting, and rollback strategies.

Course Format

• Instructor-guided presentations combined with technical deep dives.
• Hands-on exercises focused on building real-world CI/CD workflows.
• Live-lab practice for deploying ML workloads to Kubernetes.

Course Customization Options

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