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

This instructor-led, live training in Kenya (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.

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

By the conclusion 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 containerization platform that facilitates consistent, portable, and reproducible environments for AI and machine learning workloads.

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

Upon completing this course, participants will be equipped to:

• Construct and manage Docker images customized for AI and ML applications.
• Containerize machine learning pipelines, tools, and dependencies.
• Optimize Docker environments to enhance performance and portability.
• Deploy containerized ML services across various runtime environments.

Course Format

• Conceptual demonstrations accompanied by guided discussions.
• Hands-on exercises targeting real-world containerization tasks.
• Practical implementation within live-lab Docker environments.

Course Customization Options

• To tailor this training to your organizational needs, please reach out to us to coordinate.

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

This instructor-led training, available both online or onsite, targets intermediate-level professionals looking to automate end-to-end AI model delivery workflows using Docker and CI/CD platforms.

Upon completing the training, participants will be able to:

• Establish automated pipelines for building and testing AI model containers.
• Enforce version control and reproducibility across model lifecycles.
• Incorporate automated deployment strategies for AI services.
• Apply CI/CD best practices specifically tailored to 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 organisation require customised pipeline workflows or platform integrations, please reach out to us to tailor this course accordingly.

The Certified Kubernetes Administrator (CKA) certification was established by The Linux Foundation in collaboration with the Cloud Native Computing Foundation (CNCF).

Today, Kubernetes stands as a premier 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 performance in Kubernetes 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.

Additionally, the training emphasizes 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.
• Ample 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 it.
• For more information about CKA certification, please visit: https://training.linuxfoundation.org/certification/certified-kubernetes-administrator-cka

The Certified Kubernetes Application Developer (CKAD) programme was developed by The Linux Foundation and the Cloud Native Computing Foundation (CNCF), the host organisation of Kubernetes.

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

Furthermore, the training emphasises gaining practical experience in Kubernetes application development. Therefore, we recommend participating even if you do not intend to sit for the CKAD exam.

NobleProg has been delivering Docker & Kubernetes training since 2015. With over 360 successfully completed training projects, we have become one of the most recognised training companies globally in the field of containerisation. Since 2019, we have also assisted our customers in validating their performance in k8s environments by preparing them and encouraging them to pass the CKA and CKAD exams.

Format of the Course

• Interactive lectures and discussions.
• Extensive exercises and practice.
• 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/

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

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

Upon completing this training, participants will be able to:

• Install and configure Docker.
• Comprehend and implement software containerization.
• Manage applications based on Docker.
• Connect various Docker applications and systems via networking.
• Understand and modify Docker registries.

Format of the Course

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

Course Customization Options

• To request customized training for this course, please get in touch with us to arrange it.
• To learn more about Docker, please 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 gain the skills needed to operate modern container platforms and troubleshoot applications across development and production environments.

In this instructor-led live training in Kenya (onsite or remote), participants will learn how to create and manage Docker containers before deploying a sample application inside one. Attendees will also discover how to automate, scale, and manage their containerized applications within a Kubernetes cluster. The training progresses to more advanced topics, guiding participants through the processes 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.

Course Objectives: Gain 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 is addressed to participants with varied backgrounds, ensuring a complete scope of both basic and advanced Kubernetes features. It includes key concepts such as Pods, Labels, Controllers, Services, Secrets, Persistent Data Volumes, Claims, Namespaces, Quotas, Container Networking Model, Service Discovery, Scaling, Load Balancing, Cluster Management, Kubernetes Installation, Cluster Security, Access Control, Controller High Availability, Monitoring and Logging, Autoscaling, 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 construction, training, and deployment of machine learning workloads on Kubernetes.

This instructor-led, live training (available online or on-site) is tailored for beginner to intermediate-level professionals aiming to establish reliable ML workflows using Kubeflow.

Upon completing this training, participants will acquire the following skills:

• Navigating the Kubeflow ecosystem and its core components.
• Creating reproducible workflows via Kubeflow Pipelines.
• Executing scalable training jobs on Kubernetes.
• Efficiently serving machine learning models using Kubeflow Serving.

Course Format

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

Course Customization Options

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

This instructor-led, live training in Kenya, delivered online or onsite, targets developers and data scientists seeking to construct, deploy, and maintain machine learning workflows on Kubernetes.

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

• Install and configure Kubeflow in on-premise and cloud environments.
• Build, deploy, and manage ML workflows leveraging Docker containers and Kubernetes.
• Operate entire machine learning pipelines across diverse architectures and cloud environments.
• Use Kubeflow to spawn and manage Jupyter notebooks.
• Develop ML training, hyperparameter tuning, and serving workloads across multiple platforms.

This instructor-led, live training in Kenya (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 either online or onsite, is designed for intermediate-level practitioners aiming to build automated, scalable MLOps pipelines on Kubernetes.

Upon completing this training, participants will be able to:

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

Course Format

• Instructor-guided presentations and technical deep dives.
• Hands-on exercises to 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.