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

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

This instructor-led, live training, available either online or onsite, targets intermediate-level professionals aiming to package machine learning codebases, dependencies, and models using Docker to ensure reliable workflows from development to production.

Upon completing this course, participants will be equipped to:

• Create and manage Docker images specifically designed for AI and ML applications.
• Containerize machine learning pipelines, tools, and dependencies.
• Optimize Docker environments for enhanced performance and portability.
• Deploy containerized ML services across various runtime environments.

Course Format

• Concept demonstrations supported by guided discussions.
• Hands-on exercises centered on real-world containerization tasks.
• Practical implementation utilizing live-lab Docker environments.

Course Customization Options

• To tailor this training to your organizational environment, please contact us to arrange.

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

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

Upon completion of the training, participants will be capable of:

• Establishing automated pipelines for the construction and testing of AI model containers.
• Ensuring version control and reproducibility throughout the model lifecycle.
• Integrating automated deployment strategies for AI services.
• Applying CI/CD best practices specifically adapted for 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 within 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) 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/

This instructor-led, live training in Kenya (online or onsite) is designed for engineers who aim to utilize Docker for deploying and managing software as containers, moving away from traditional standalone software models.

After 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 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.

Docker serves as a containerization 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 aiming to containerize and operationalize complete ML pipelines using Docker.

Upon completing this training, participants will be able to:

• Containerize ML training, validation, and inference workloads.
• Design and orchestrate end-to-end ML pipelines using Docker and supporting tools.
• Implement versioning, reproducibility, and CI/CD for ML components.
• Deploy, monitor, and scale ML services in containerized environments.

Format of the Course

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

Course Customization Options

• For customized training aligned with specific ML infrastructure needs, please contact us to discuss options.

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.

Leveraging GPU acceleration is crucial for executing high-performance deep learning tasks efficiently and at scale.

This live, instructor-led training (available online or on-site) targets intermediate technical professionals aiming to configure, optimize, and execute GPU-enabled AI workloads within Docker containers.

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

• Construct and operate GPU-enabled containers for both training and inference processes.
• Set up CUDA, drivers, and runtime libraries tailored for containerized AI workflows.
• Enhance resource allocation and isolation for applications that are intensive in GPU usage.
• Deploy scalable, containerized deep learning services within production settings.

Course Format

• Interactive teaching reinforced with practical, real-world demonstrations.
• Practical exercises centered on GPU-enabled development.
• Direct implementation in a live-lab environment.

Customization Options

• For bespoke training tailored to your specific infrastructure or GPU stack, please get in touch with us to arrange it.

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

This instructor-led live training, available both online and onsite, is designed for advanced professionals seeking to design and deploy distributed AI inference systems across diverse environments.

Upon completing this training, participants will be able to:

• Develop secure and scalable containerized 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.
• Enhance inference latency, reliability, and resilience across varied infrastructure.

Course Format

• Guided presentations complemented by expert-led discussions.
• Extensive hands-on practice through applied exercises.
• Real-world experimentation within a controlled live-lab environment.

Customization Options

• To tailor this course to your organization’s specific infrastructure or use cases, please contact us to arrange customization.

In this instructor-led, live training Kenya (onsite or remote), participants will learn how to deploy a collection of sample servers inside containers, then automate, scale, and manage their containerized servers within a Kubernetes cluster. The training goes on to more advanced topics, walking participants through the process of securing, networking and monitoring a Kubernetes cluster.

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

• Set up and run a Docker container.
• Deploy containerized databases and servers.
• Set up a Docker and Kubernetes cluster.
• Use Kubernetes to deploy and manage different environments under the same cluster.
• Secure, scale and monitor a Kubernetes cluster.

In this instructor-led live training held in Kenya, participants will learn the essentials of building microservices using Spring Cloud and Docker. Knowledge will be tested through exercises and the incremental development of sample microservices.

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

• Understand the fundamental principles of microservices.
• Use Docker to build containers for microservice applications.
• Build and deploy containerized microservices using Spring Cloud and Docker.
• Integrate microservices with discovery services and the Spring Cloud API Gateway.
• Use Docker Compose for end-to-end integration testing.