Containerized AI & ML Deployment with Docker Training Course

This instructor-led, live training in Uzbekistan (online or on-site) is intended for engineers who wish to deepen their Docker expertise to deploy applications at scale while maintaining full control.

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

• Build their own Docker images.
• Deploy and manage large numbers of Docker applications.
• Evaluate various container orchestration solutions and select the most appropriate one.
• Establish a continuous integration pipeline for Docker applications.
• Integrate Docker applications with existing continuous integration tooling.
• Secure their Docker applications.

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

• Creating custom Docker images.
• Deploying and managing a large number of Docker applications.
• Evaluating various container orchestration solutions to select the most appropriate one.
• Establishing a continuous integration process for Docker applications.
• Integrating Docker applications with existing continuous integration toolchains.
• Implementing security measures for Docker applications.
• Utilizing Kubernetes to deploy and manage diverse environments within a single cluster.
• Securing, scaling, and monitoring a Kubernetes cluster.

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

This instructor-led training session, available both online and onsite, is designed for professionals with an intermediate level of expertise who aim to streamline end-to-end AI model delivery workflows using Docker and CI/CD platforms.

Upon completing this course, participants will be equipped to:

• Develop automated pipelines for constructing and evaluating AI model containers.
• Establish version control and ensure reproducibility 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 coupled with technical discussions.
• Hands-on labs and practical implementation exercises.
• Realistic simulations of CI/CD workflows within a controlled environment.

Customization Options

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

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

Kubernetes has become the leading 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 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 to take and 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.

Additionally, the training focuses on gaining practical experience in Kubernetes Administration. Therefore, we recommend participating even if you do not intend to take the CKA exam.

Course Format

• 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 CKA certification, please visit: https://training.linuxfoundation.org/certification/certified-kubernetes-administrator-cka

The Certified Kubernetes Application Developer (CKAD) program 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, the training also focuses on gaining practical experience in Kubernetes application development, so we recommend participating even if you do not intend to take the CKAD exam.

NobleProg has been delivering Docker & Kubernetes training since 2015. With more than 360 successfully completed training projects, we have become one of the best-known training companies worldwide in the field of containerization. Since 2019, we have also been helping our customers validate their performance in k8s environments by preparing them and encouraging them to pass CKA and CKAD exams.

Course Format

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

Course Customization Options

• To request a customized version of 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 construction, distribution, and execution of applications within containers.

This instructor-led live training, available online or on-site, targets engineers who want to leverage Docker for deploying and managing software as containers rather than traditional standalone applications.

Upon completing this training, participants will be able to:

• Install and set up Docker.
• Grasp and apply the principles of software containerization.
• Manage applications hosted on Docker.
• Establish networks between various Docker applications and systems.
• Understand and modify Docker registries.

Course Format

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

Customization Options for the Course

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

Discover how to build, deploy, and administer containerized environments using Docker, Kubernetes, and OpenShift. This five-day, interactive training spans container images, Kubernetes workloads, cluster networking, storage solutions, security measures, monitoring techniques, and practical OpenShift administration. Participants will acquire the necessary skills to operate modern container platforms and troubleshoot applications across both development and production settings.

Through this instructor-led live training in Uzbekistan (onsite or remote), participants will acquire the skills to create and manage Docker containers, as well as deploy a sample application within one. The curriculum also covers automating, scaling, and managing containerized applications inside a Kubernetes cluster. Furthermore, the training progresses to advanced topics, guiding participants through securing, scaling, and monitoring a Kubernetes cluster.

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

• Set up and operate a Docker container.
• Deploy a containerized server and web application.
• Construct and oversee Docker images.
• Establish a Docker and Kubernetes cluster.
• Utilize Kubernetes to deploy and manage a clustered web application.
• Secure, scale, and monitor a Kubernetes cluster.

Docker serves as a containerization platform that enables the creation of reproducible, portable, and scalable environments tailored for machine learning systems.

This live, instructor-led training—available online or onsite—is designed for intermediate to advanced technical professionals seeking to operationalize and containerize complete ML pipelines using Docker.

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

• Encapsulate ML training, validation, and inference workloads within containers.
• Architect and orchestrate comprehensive ML pipelines leveraging Docker alongside complementary tools.
• Establish version control, reproducibility, and CI/CD practices for ML components.
• Deploy, monitor, and scale ML services within containerized infrastructures.

Course Structure

• Engaging lectures reinforced by practical demonstrations.
• Practical exercises centered on constructing real-world ML pipeline elements.
• Live-lab sessions focused on implementing end-to-end containerized workflows.

Customization Possibilities

• To tailor the training to specific ML infrastructure requirements, please reach out to us to explore available options.

Training objectives: Acquire both 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 designed for participants with varying levels of experience, offering a full range of both fundamental and advanced Kubernetes functionalities. Key topics include Containers, Labels, Controllers, Services, Secrets, Persistent Volumes, Claims, Namespaces, Resource Quotas, the Container Networking Model, Service Discovery, Scaling, Load Balancing, Cluster Management, Kubernetes Installation, Cluster Security, Access Control, High Availability of Control Planes, Monitoring and Logging, Self-Healing of Applications, Advanced Scheduling, Microservices-Based Applications, Application Design Patterns, and Deployment of Applications on Kubernetes Clusters.

To execute high-performance deep learning tasks with both scalability and efficiency, GPU acceleration is a critical requirement.

Designed for intermediate-level technical experts, this instructor-led live session (available online or on-site) focuses on setting up, fine-tuning, and managing GPU-powered AI workloads within Docker containers.

By the end of the training, participants will be equipped to:

• Construct and execute GPU-enabled containers for both training and inference processes.
• Configure CUDA, drivers, and runtime libraries specifically for containerized AI pipelines.
• Enhance resource allocation and ensure proper isolation for applications with heavy GPU demands.
• Implement scalable, containerized deep learning services suitable for production environments.

Course Delivery Format

• Engaging instruction complemented by practical, real-world examples.
• Practice exercises centered on GPU-enabled development tasks.
• Practical implementation within a live-lab setting.

Customization Opportunities

• To align the training with your specific infrastructure or GPU stack, please reach out to us for arrangements.

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

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

After completing this training, participants will be capable of:

• Creating secure and scalable containerized AI services for multi-location setups.
• Deploying AI inference workloads to cloud platforms, local servers, and edge devices using Docker.
• Integrating orchestration tools to automate distributed AI operations.
• Enhancing inference latency, reliability, and resilience across diverse infrastructure.

Course Format

• Guided presentations and expert-led discussions.
• Extensive hands-on practice with applied exercises.
• Real-world experimentation in a controlled live-lab environment.

Customization Options for the Course

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

In this instructor-led, live training Uzbekistan (onsite or remote), participants will learn to deploy sample servers within containers, followed by automating, scaling, and managing these servers in a Kubernetes cluster. The training advances to sophisticated topics, guiding participants through the processes 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 in Uzbekistan, participants will learn the fundamentals of building microservices using Spring Cloud and Docker. Their knowledge will be tested through hands-on exercises and the step-by-step development of sample microservices.

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

• Understand the core 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.