Docker for MLOps: End-to-End Pipeline Containerization 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.

This instructor-led, live training in US (online or onsite) is designed for senior AI engineers and data scientists with intermediate-to-advanced expertise. The program focuses on enhancing DeepSeek model performance, reducing latency, and deploying AI solutions with efficiency and accountability for government contexts.

Upon completion, participants will be equipped to:

• Optimize DeepSeek models for maximum efficiency, precision, and scalability in public sector operations.
• Apply rigorous MLOps standards and model versioning protocols to ensure operational integrity.
• Deploy DeepSeek models on both cloud-based and on-premise infrastructure aligned with institutional requirements.
• Monitor, maintain, and scale AI solutions with a focus on reliability and performance.

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

Docker is an open-source platform designed to automate the construction, distribution, and execution of applications within containerized environments.

This instructor-led, live training (conducted online or on-site) is intended for engineers seeking to utilize Docker for the deployment and administration of software as containers, replacing traditional standalone software models for government use.

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

• Installation and configuration of Docker environments.
• Implementation of software containerization strategies.
• Management of Docker-based applications.
• Networking of Docker applications and system components.
• Interpretation and modification of Docker registries.

Training Delivery Format

• Interactive instruction and facilitated discussion.
• Extensive exercises and practical application.
• Hands-on implementation within a live laboratory environment.

Customization Options for Training

• To request a customized training curriculum for this course, please contact the relevant office for coordination.
• To access additional information regarding Docker, please visit: https://www.docker.com

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.

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

Kubeflow constitutes an open-source infrastructure intended to optimize the development, training, and deployment of machine learning operations on Kubernetes for government and public sector use.

This live, instructor-facilitated session, available remotely or in-person, targets professionals with foundational to intermediate experience who seek to establish secure and reliable machine learning workflows for government applications.

Upon successful completion of this instruction, participants will acquire the competency to:

• Operate within the Kubeflow ecosystem and utilize its core modules.
• Develop consistent and reproducible workflows utilizing Kubeflow Pipelines.
• Execute scalable training operations on Kubernetes infrastructure.
• Deliver machine learning models with high efficiency using the Kubeflow Serving framework.

Instructional Methodology

• Structured presentations and facilitated collaborative dialogue.
• Practical laboratory sessions utilizing actual Kubeflow components.
• Applied exercises focused on constructing complete machine learning workflows.

Adaptation Options for Course Content

• Tailored variations of this training program may be developed to accommodate specific agency technology stacks and mission requirements.

This instructor-led, live training session in US (available online or onsite) is designed for developers and data scientists who wish to construct, deploy, and oversee machine learning workflows on Kubernetes platforms.

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

• Install and configure Kubeflow in both on-premise and cloud environments.
• Develop, deploy, and manage ML workflows utilizing Docker containers and Kubernetes infrastructure.
• Execute complete machine learning pipelines across various architectural and cloud environments.
• Utilize Kubeflow for the instantiation and management of Jupyter notebooks.
• Establish ML training, hyperparameter tuning, and model serving workloads across multiple platforms for government operations.

This instructor-led, live training in US (online or onsite) is tailored for engineers who aim to assess contemporary approaches and tools to make strategic decisions on the adoption pathway for MLOps within their organization.

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

• Installing and configuring various MLOps frameworks and tools.
• Constructing teams with the requisite skills for building and sustaining an MLOps system.
• Preparing, validating, and versioning data for ML model utilization.
• Comprehending the components of an ML Pipeline and the instruments required for its development.
• Testing various machine learning frameworks and server infrastructure for production-grade deployment.
• Operationalizing the Machine Learning process to ensure reproducibility and maintainability for government use.

MLOps on Kubernetes serves as a structured framework for automating the training, validation, packaging, and deployment of machine learning models, utilizing containerized pipelines and GitOps workflows to support operations for government.

This instructor-led training session, available either online or onsite, is designed for intermediate-level professionals seeking to develop automated, scalable MLOps pipelines on Kubernetes.

Upon completion of this program, participants will be positioned to:

• Architect end-to-end CI/CD pipelines tailored for machine learning operations.
• Establish GitOps workflows to manage model deployment and versioning.
• Automate the training, testing, and packaging of ML models.
• Integrate comprehensive monitoring, alerting, and rollback strategies.

Course Delivery Format

• Instructor-led presentations and detailed technical analyses.
• Practical exercises focused on constructing operational CI/CD workflows.
• Live-lab sessions involving the deployment of ML workloads to Kubernetes.

Customization Capabilities

• Agencies may request tailored content that aligns with their specific internal MLOps tools and infrastructure requirements.