Secure & Portable AI Inference with Docker: From Local to Cloud 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.

Docker serves as a containerisation platform that facilitates consistent, portable, and reproducible environments for artificial intelligence and machine learning workloads.

This instructor-led, live training (available online or onsite) targets intermediate-level professionals who wish to package ML codebases, dependencies, and models using Docker to ensure reliable workflows from development to production.

Upon completing this course, participants will be capable of:

• Building and managing Docker images specifically tailored for AI and ML applications.
• Containerising machine learning pipelines, tools, and dependencies.
• Optimising Docker environments for performance and portability.
• Deploying containerised ML services across various runtime environments.

Course Format

• Concept demonstrations supported by guided discussion.
• Practical exercises focused on real-world containerisation tasks.
• Hands-on implementation using live-lab Docker environments.

Course Customisation Options

• To customise this training for your organisational environment, please contact us to arrange.

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

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

Upon completing the training, participants will be capable of:

• Establishing automated pipelines for the construction and testing of 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 tailored to machine learning operations.

Course Format

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

Course Customization Options

• Should your organization require 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/

This instructor-led, live training in Botswana (online or onsite) is tailored for engineers who want to adopt Docker 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.

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.

Docker serves as a containerisation platform designed to create reproducible, portable, and scalable environments for machine learning systems.

This instructor-led, live training (available online or onsite) targets intermediate to advanced technical professionals seeking to containerise and operationalise complete machine learning pipelines using Docker.

Upon completing this training, participants will be equipped to:

• Containerise machine learning training, validation, and inference workloads.
• Design and orchestrate end-to-end machine learning pipelines using Docker and complementary tools.
• Implement versioning, reproducibility, and continuous integration and delivery (CI/CD) for machine learning components.
• Deploy, monitor, and scale machine learning services within containerised environments.

Format of the Course

• Interactive lectures supported by practical demonstrations.
• Hands-on exercises focused on building real machine learning pipeline components.
• Live-lab implementation for end-to-end containerised workflows.

Course Customisation Options

• For customised training aligned with specific machine learning infrastructure needs, please contact us to discuss options.

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.

GPU acceleration is vital 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 who want to configure, optimise, and operate GPU-enabled AI workloads within Docker containers.

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

• Build and run GPU-enabled containers for training and inference.
• Configure CUDA, drivers, and runtime libraries for containerised AI workflows.
• Optimise resource allocation and isolation for GPU-intensive applications.
• Deploy scalable, containerised deep learning services in production environments.

Course Format

• Interactive instruction supported by real-world demonstrations.
• Exercise-driven practice focused on GPU-enabled development.
• Hands-on implementation in a live-lab environment.

Course Customisation Options

• For tailored training aligned with your infrastructure or GPU stack, please contact us to arrange.

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

This instructor-led, live training session (available online or onsite) is designed for advanced professionals aiming to design and deploy distributed AI inference systems across heterogeneous environments.

Upon completing this training, participants will be able to:

• Construct secure and scalable containerised AI services for multi-site environments.
• Deploy AI inference workloads to cloud platforms, local servers, and edge devices using Docker.
• Integrate orchestration tools to automate distributed AI operations.
• Optimise 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 within a controlled live-lab environment.

Course Customisation Options

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

In this instructor-led, live training session in Botswana (onsite or remotely), participants will learn how to deploy a range of sample servers inside containers and subsequently automate, scale, and manage these containerised servers within a Kubernetes cluster. The training progresses to more advanced topics, guiding participants through the processes of securing, networking, and monitoring a Kubernetes cluster.

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

• Establish and operate a Docker container.
• Deploy containerised databases and servers.
• Set up a Docker and Kubernetes cluster.
• Utilise Kubernetes to deploy and manage various environments within the same cluster.
• Secure, scale, and monitor a Kubernetes cluster.

In this instructor-led, live training in Botswana, participants will learn the core principles of building microservices using Spring Cloud and Docker. Participant knowledge is assessed through exercises and the step-by-step development of sample microservices.

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

• Comprehend the fundamentals of microservices.
• Utilise Docker to build containers for microservice applications.
• Construct and deploy containerised microservices using Spring Cloud and Docker.
• Integrate microservices with discovery services and the Spring Cloud API Gateway.
• Employ Docker Compose for end-to-end integration testing.