Introduction to Docker Training Course

This instructor-led, live training conducted in US (either online or on-site) is designed for engineers aiming to deepen their expertise in Docker to support the deployment of applications at scale while maintaining robust control.

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

• Construct custom Docker images.
• Deploy and manage large-scale Docker applications.
• Evaluate diverse container orchestration solutions to determine the most suitable framework.
• Implement continuous integration workflows for Docker applications.
• Integrate Docker applications into existing continuous tool integration processes.
• Enhance the security posture of Docker applications.

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

• Develop and customize proprietary Docker images.
• Deploy and oversee a substantial volume of Dockerized applications.
• Assess various container orchestration platforms and select the optimal solution for specific requirements.
• Establish continuous integration pipelines tailored for Docker applications.
• Integrate Docker workflows with existing continuous integration toolchains.
• Implement robust security measures for Docker-based applications.
• Leverage Kubernetes to manage diverse environments within a unified cluster.
• Ensure the security, scalability, and observability of Kubernetes clusters.

Continuous Integration and Continuous Delivery (CI/CD) for AI represents a systematic methodology for automating model packaging, testing, containerization, and deployment via integrated pipelines.

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

Upon completion, participants will be equipped to:

• Develop automated pipelines for constructing and testing AI model containers.
• Enforce version control and reproducibility standards across model lifecycles.
• Incorporate automated deployment strategies for AI services for government operations.
• Apply CI/CD best practices aligned with machine learning operations requirements.

Course Delivery Format

• Instructor-led presentations and technical discourse.
• Practical laboratories and hands-on implementation exercises.
• Simulations of realistic CI/CD workflows within a controlled environment.

Course Customization Opportunities

• For organizations requiring bespoke pipeline workflows or platform integrations, contact us to tailor this course.

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

Kubernetes has emerged as a predominant platform for container orchestration.

NobleProg has provided Docker and Kubernetes training since 2015. With over 360 successfully completed training engagements, the organization is recognized globally for its expertise in containerization.

Since 2019, NobleProg has supported clients in validating their technical proficiency within Kubernetes environments by facilitating preparation for the CKA and CKAD examinations.

This instructor-led, live training session (available online or onsite) is designed for System Administrators and Kubernetes practitioners who seek to validate their competencies through the CKA examination.

Furthermore, the curriculum emphasizes practical experience in Kubernetes administration; therefore, participation is recommended for individuals seeking to enhance operational capabilities, irrespective of their intent to pursue CKA certification for government initiatives.

Course Format

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

Course Customization Options

• Requests for customized training programs for this course may be directed to NobleProg for scheduling.
• Detailed information regarding CKA certification is available at: https://training.linuxfoundation.org/certification/certified-kubernetes-administrator-cka

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 for government and troubleshoot applications across development and production environments.

In this instructor-led training session held in US (onsite or remote), participants will acquire the technical proficiency to create and manage Docker containers, including the deployment of representative applications. The curriculum further instructs on the automation, scaling, and governance of containerized applications within a Kubernetes cluster. The training concludes with advanced topics, guiding participants through the rigorous processes of securing, scaling, and monitoring Kubernetes clusters to meet operational standards.

Upon completion of this training, participants will possess the following capabilities:

• Initialization and execution of Docker containers.
• Deployment of containerized server and web application stacks.
• Construction and administration of Docker image repositories.
• Configuration of Docker and Kubernetes cluster infrastructures.
• Application of Kubernetes to deploy and oversee clustered web services.
• Enforcement of security protocols, scaling adjustments, and continuous monitoring of Kubernetes clusters.

Docker serves as a containerization technology enabling the creation of consistent, portable, and scalable environments for machine learning systems in the public sector.

This instructor-led training session, available remotely or in person, targets intermediate to advanced technical staff seeking to containerize and operationalize full-scale machine learning pipelines for government use.

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

• Encapsulate machine learning training, validation, and inference tasks.
• Design and manage comprehensive machine learning workflows using Docker and complementary tools.
• Establish version control, reproducibility, and CI/CD practices for machine learning components.
• Deploy, oversee, and scale machine learning services within containerized frameworks.

Course Format

• Interactive instruction accompanied by practical application demonstrations.
• Practical exercises centered on developing functional machine learning pipeline elements.
• Live laboratory sessions for executing end-to-end containerized operations.

Customization Opportunities

• To align training with specific government machine learning infrastructure requirements, please contact the provider for detailed options.

Training objectives: Develop theoretical knowledge and operational proficiency in Docker and Kubernetes technologies.

The "Advanced Docker and Kubernetes" instruction offers a thorough analysis of the Kubernetes platform and its surrounding ecosystem. Tailored for participants with varied levels of technical proficiency, the curriculum covers a complete spectrum of fundamental and advanced features. The course addresses essential concepts including Pods, Labels, Controllers, Services, Secrets, Persistent Data Volumes, Claims, Namespaces, and Resource Quotas. It further examines the Container Networking Model, Service Discovery, Scaling mechanisms, and Load Balancing strategies. Additional topics include Cluster Management, Kubernetes installation procedures, security protocols for cluster protection, access control frameworks, High Availability of the Control Plane, monitoring and logging standards, automatic application scaling, advanced scheduling logic, microservices-based application architectures, specific application design patterns, and the deployment of applications and services on a Kubernetes cluster for government use.

GPU acceleration is a critical component for executing high-performance deep learning workloads with scalable and efficient resource management.

This instructor-led, live training program (available online or on-site) is designed for intermediate-level technical professionals tasked with configuring, optimizing, and executing GPU-enabled AI workloads within Docker containers for government use.

Upon completion of this course, participants will be equipped to:

• Construct and execute GPU-enabled containers for both training and inference phases.
• Configure CUDA, system drivers, and runtime libraries for containerized AI workflows.
• Optimize resource allocation and enforce isolation protocols for GPU-intensive applications.
• Deploy scalable, containerized deep learning services within production environments.

Course Delivery Format

• Interactive instruction reinforced by practical, real-world demonstrations.
• Exercise-based practice centered on GPU-enabled development standards.
• Hands-on implementation conducted in a secure live-lab environment.

Course Customization Options

• For tailored training aligned with specific infrastructure or GPU stack requirements, please contact us to arrange a schedule.

Hybrid AI implementation involves executing AI inference across cloud, on-premise, and edge domains through standardized container-based procedures.

This instructor-led, live training module (available online or on-site) is designed for senior-level professionals seeking to architect and implement distributed AI inference systems across diverse environments.

Upon completion of this course, participants will possess the ability 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 coordination tools to automate distributed AI operations for government purposes.
• Enhance inference speed, dependability, and system resilience across varied infrastructure.

Course Delivery Structure

• Structured presentations and expert-facilitated dialogue.
• Substantial hands-on practice and applied laboratory exercises.
• Practical experimentation within a controlled, live laboratory environment.

Course Adaptation Options

• To modify this course to align with specific organizational infrastructure or operational use cases, please contact us to customize the training for government requirements.

This instructor-led, live training is conducted in US (available onsite or remotely). The curriculum focuses on deploying sample server applications within containers, followed by the automation, scaling, and management of these workloads within a Kubernetes cluster. The course addresses advanced operational topics, guiding participants through the critical processes of securing, networking, and monitoring Kubernetes clusters.

Upon completion, participants will be capable of:

• Initializing and operating Docker containers.
• Deploying containerized databases and server applications.
• Establishing and configuring Docker and Kubernetes clusters.
• Utilizing Kubernetes to manage distinct operational environments within a unified cluster.
• Implementing security measures, scaling protocols, and monitoring systems for Kubernetes clusters.

In this instructor-led, live training session conducted in US, participants will acquire the essential skills for constructing microservices utilizing Spring Cloud and Docker. Learning outcomes are validated through rigorous exercises and the systematic development of sample microservice components.

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

• Analyzing the core principles of microservice architecture.
• Employing Docker to engineer containerized environments for microservice applications.
• Implementing and deploying containerized microservices using Spring Cloud and Docker frameworks.
• Establishing integration between microservices, discovery services, and the Spring Cloud API Gateway.
• Conducting comprehensive end-to-end integration testing via Docker Compose.