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

This instructor-led, live training in Botswana (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 Botswana (online or onsite) targets advanced AI engineers and data scientists with intermediate to advanced proficiency who aim to improve DeepSeek model performance, reduce latency, and implement AI solutions efficiently through contemporary MLOps practices.

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

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

Docker serves as a powerful containerization platform that ensures consistent, portable, and reproducible environments for artificial intelligence and machine learning tasks.

This live, instructor-led session, available online or in person, is designed for intermediate professionals looking to package ML codebases, dependencies, and models using Docker to achieve reliable workflows from development through to production.

Upon successfully completing this course, participants will be equipped to:

• Create and maintain Docker images specifically optimized for AI and ML applications.
• Encapsulate machine learning pipelines, associated tools, and dependencies within containers.
• Tune Docker environments to enhance performance and ensure portability.
• Roll out containerized ML services across various runtime environments.

Course Structure

• Conceptual demonstrations accompanied by guided discussions.
• Practical exercises centered on real-world containerization challenges.
• Hands-on application using live-lab Docker environments.

Tailoring the Course Content

• To align this training with your specific organizational needs, please reach out to discuss customization options.

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

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

Upon completion of the training, 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 organisation requires customized pipeline workflows or platform integrations, please contact us to tailor this course.

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

Kubernetes has emerged as the premier platform for container orchestration.

NobleProg has been providing Docker and 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 skills within 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.
• Hands-on implementation in a live-lab environment.

Course Customisation Options

• To request customised training for this course, please contact us to arrange.
• To learn more about the CKA certification, please visit: https://training.linuxfoundation.org/certification/certified-kubernetes-administrator-cka

The Certified Kubernetes Application Developer (CKAD) programme has been 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 creation, distribution, and execution of applications within containers.

This instructor-led live training (available online or in person) is tailored for engineers who want to deploy and manage software as containers rather than relying on traditional standalone methods.

Upon completing this training, participants will be capable of:

• Installing and configuring Docker.
• Gaining a solid grasp of and implementing software containerisation.
• Managing applications based on Docker.
• Networking diverse Docker applications and systems.
• Understanding and editing Docker registries.

Course Format

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

Course Customisation Options

• To request customised training for this course, please contact us to make arrangements.
• For more information on Docker, please visit: https://www.docker.com

Learn how to build, deploy, and administer containerised 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 Botswana (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 targets participants with varying levels of experience, ensuring a full range of both basic and advanced Kubernetes features. It includes key concepts such as Containers, Labels, Controllers, Services, Secrets, Persistent Data 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-scaling, Advanced Scheduling, Microservices-based Applications, Application Design Patterns, and Application Deployment on a Kubernetes Cluster.

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 live, instructor-led training held in Botswana (either online or onsite) is specifically designed for developers and data scientists seeking to build, deploy, and manage machine learning workflows on Kubernetes.

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

• Install and configure Kubeflow in both on-premise and cloud-based settings.
• Create, deploy, and manage ML workflows utilizing Docker containers and Kubernetes.
• Execute complete machine learning pipelines across various architectures and cloud environments.
• Leverage Kubeflow to launch and control Jupyter notebooks.
• Develop ML training, hyperparameter tuning, and serving workloads spanning multiple platforms.

This instructor-led live training in Botswana (online or onsite) is designed for engineers who wish to evaluate the approaches and tools currently available, helping them make informed decisions on the path forward for adopting MLOps within their organisation.

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 offers 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) is designed for intermediate-level practitioners who aim to build automated, scalable MLOps pipelines on Kubernetes.

Upon completion of 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 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.