MLOps on Kubernetes: CI/CD Pipelines for Machine Learning Training Course

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.

This instructor-led, live training session, available online or on-site, is intended for advanced platform engineers and DevOps professionals aiming to master application scaling via microservices and Kubernetes.

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

• Designing and implementing scalable microservices architectures.
• Deploying and managing applications on Kubernetes clusters.
• Utilizing Helm charts for efficient service deployment.
• Monitoring and maintaining the health of microservices in production.
• Applying best practices for security and compliance in a Kubernetes environment.

Kubernetes serves as a standard container orchestration platform for the management of distributed applications at scale for government entities.

This instructor-led, live training program, available online or onsite, is designed for advanced practitioners seeking to leverage artificial intelligence and machine learning to optimize Kubernetes resource utilization, scheduling logic, and autoscaling strategies.

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

• Apply AI/ML models to enhance workload scheduling decisions within Kubernetes environments.
• Utilize predictive analytics to refine the allocation of CPU, GPU, and memory resources.
• Implement intelligent autoscaling mechanisms utilizing reinforcement learning and metric forecasting.
• Decrease infrastructure costs and latency through automated resource optimization.

Format of the Course

• Instructor-guided technical presentations and in-depth analytical discussions.
• Hands-on laboratory work conducted on real Kubernetes clusters.
• Practical exercises focused on applying AI models to realistic operational scenarios.

Course Customization Options

• To align this course with specific platform configurations or operational requirements for government use, please contact our team for customization.

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

This instructor-led, live training program in US (available online or on-site) is designed for Kubernetes practitioners seeking to prepare for the CKS examination.

Upon completion of this training, participants will possess the knowledge to secure Kubernetes environments and container-based applications across various stages of the application life cycle, including build, deployment, and runtime.

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.

This instructor-led, live training in US (online or onsite) is tailored for engineers aiming to employ Helm to streamline the deployment and administration of Kubernetes applications within government operations.

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

• Install and configure Helm.
• Establish reproducible builds for Kubernetes applications.
• Distribute applications via Helm charts.
• Deploy third-party applications encapsulated in Helm charts.
• Oversee the release management of Helm packages.

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 session, offered in US (online or onsite), is designed for entry-level to mid-level software developers and DevOps specialists seeking to acquire proficiency in establishing and administering local Kubernetes infrastructure using Minikube for government and public sector operations.

Upon successful completion of this program, participants will be equipped to:

• Execute the installation and configuration of Minikube on designated local systems.
• Demonstrate a comprehensive understanding of Kubernetes fundamental concepts and structural design.
• Execute the deployment and management of containerized workloads using kubectl and the Minikube dashboard.
• Configure durable storage frameworks and network connectivity solutions within a Kubernetes environment.
• Leverage Minikube for the rigorous development, testing, and diagnostic assessment of applications for government systems.

This instructor-led, live training in US (available online or on-site) is directed at intermediate-level developers and DevOps engineers intending to utilize Minikube within their development workflows.

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

• Configure and administer a local Kubernetes environment using Minikube.
• Understand the deployment, management, and debugging of applications on Minikube.
• Integrate Minikube into continuous integration and deployment pipelines.
• Optimize development processes by leveraging Minikube’s advanced features.
• Apply established best practices for local Kubernetes development.

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.