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

Upon completion of this instructional module, participants will demonstrate proficiency in the following areas:

• Construct custom Docker container images.
• Deploy and manage high volumes of Docker-based applications.
• Assess various container orchestration platforms to select the most appropriate solution for organizational needs.
• Establish continuous integration workflows tailored for Docker environments.
• Integrate Docker applications with existing CI/CD toolchains.
• Implement security best practices for Docker workloads.
• Utilize Kubernetes to deploy and manage diverse environments within a single cluster.
• Secure, scale, and monitor Kubernetes clusters effectively.

This instructor-led, live training US (online or onsite) is designed for advanced-level AI engineers and data scientists with intermediate-to-advanced experience who wish to enhance DeepSeek model performance, minimize latency, and deploy AI solutions efficiently using modern MLOps practices. This program provides essential capabilities for government agencies seeking robust artificial intelligence frameworks.

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

• Optimize DeepSeek models for efficiency, accuracy, and scalability.
• Implement best practices for MLOps and model versioning.
• Deploy DeepSeek models on cloud and on-premise infrastructure.
• Monitor, maintain, and scale AI solutions effectively.

This instructor-led, live training in US (online or onsite) is intended for advanced-level platform engineers and DevOps professionals seeking to master the scaling of applications through microservices and Kubernetes. The curriculum is specifically designed for government agencies and public sector entities requiring robust technical expertise.

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

• Architecting and implementing scalable microservices infrastructures.
• Deploying and managing applications within Kubernetes clusters.
• Leveraging Helm charts to streamline service deployment processes.
• Monitoring and sustaining the operational health of microservices in production environments.
• Implementing security and compliance best practices within Kubernetes ecosystems.

Kubernetes serves as a leading container orchestration system extensively adopted for managing distributed applications in large-scale environments.

This instructor-led training program, available both online and onsite, is designed for advanced technical personnel seeking to apply artificial intelligence and machine learning techniques to optimize resource utilization, scheduling logic, and autoscaling protocols in Kubernetes.

Upon successful completion of this curriculum, participants will possess the ability to:

• Integrate AI/ML models to enhance workload scheduling accuracy in Kubernetes environments.
• Leverage predictive analytics to refine the allocation of CPU, GPU, and memory resources.
• Implement intelligent autoscaling mechanisms utilizing reinforcement learning and metric forecasting.
• Minimize infrastructure costs and latency through automated resource optimization processes.

Instructional Format

• Facilitated technical briefings and detailed analytical discussions.
• Practical laboratory exercises utilizing active Kubernetes clusters.
• Applied scenarios demonstrating the implementation of AI models in real-world operations.

Curriculum Customization Provisions

• To adapt this training to specific platform configurations or operational mandates, interested parties are advised to contact the provider for tailored modifications.

The Certified Kubernetes Administrator (CKA) credential is administered by The Linux Foundation in partnership with the Cloud Native Computing Foundation (CNCF).

Kubernetes has established itself as a premier platform for container orchestration.

NobleProg has provided Docker and Kubernetes instruction since 2015. Having successfully completed over 360 training initiatives, the organization is recognized globally as a leading provider of expertise in containerization solutions for government and public sector workflows.

Since 2019, we have assisted clients in validating their Kubernetes proficiency by preparing them for the CKA and CKAD certification examinations.

This instructor-led live training, available either online or onsite, is designed for system administrators and Kubernetes practitioners seeking to validate their expertise through the CKA examination process.

Additionally, the curriculum emphasizes practical administrative skills; therefore, participation is recommended even for those who do not plan to pursue official certification.

Course Format

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

Customization Options

• To request customized training for this course, please contact us to arrange.
• For more information regarding CKA certification, visit: https://training.linuxfoundation.org/certification/certified-kubernetes-administrator-cka

The Certified Kubernetes Application Developer (CKAD) curriculum was established by The Linux Foundation and the Cloud Native Computing Foundation (CNCF), which hosts Kubernetes.

This instructor-led live training, available online or on-site, is designed for Developers seeking to validate their proficiency in designing, building, configuring, and exposing cloud native applications within Kubernetes environments.

Furthermore, the program emphasizes practical application development skills; participation is recommended for professional development, regardless of an intent to sit for the CKAD examination.

NobleProg has provided Docker and Kubernetes training since 2015. With over 360 successfully completed projects, the organization has become a leading provider globally in containerization education. Since 2019, we have assisted clients in demonstrating their capabilities within k8s environments by preparing them to pass the CKA and CKAD exams.

Course Format

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

Customization Options

• To arrange customized training for this course, please contact our team.
• For additional information regarding CKAD, please visit: https://training.linuxfoundation.org/certification/certified-kubernetes-application-developer-ckad/

This professional development resource is designed for government agencies and contractors seeking to enhance their technical capabilities.

This guided educational program in US (available via remote or in-person modalities) is designed for Kubernetes professionals preparing for the Certified Kubernetes Security Specialist (CKS) certification. The course content is specifically curated for government agencies and entities requiring rigorous security standards.

Upon completion, attendees will be equipped to implement robust security controls for Kubernetes clusters and containerized applications across all lifecycle phases, including build, deployment, and runtime operations.

Docker serves as a containerization platform for establishing reproducible, portable, and scalable environments for ML systems.

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

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

• Containerize ML training, validation, and inference workloads.
• Design and orchestrate comprehensive ML pipelines using Docker and complementary tools.
• Implement versioning, reproducibility, and CI/CD protocols for ML components.
• Deploy, monitor, and scale ML services within containerized environments.

Instructional Format

• Interactive lectures accompanied by practical demonstrations.
• Hands-on exercises dedicated to constructing functional ML pipeline components.
• Live-lab implementation for end-to-end containerized workflows.

Customization Options

• For training tailored to specific ML infrastructure requirements, please contact our office to discuss available options.

This instructor-led, live training in US (online or onsite) is aimed at engineers who wish to use Helm to streamline the process of installing and managing Kubernetes applications.

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

• Install and configure Helm.
• Create reproducible builds of Kubernetes applications.
• Share applications as Helm charts.
• Run third-party applications saved as Helm charts.
• Manage releases of Helm packages.

Kubeflow is an open-source platform engineered to facilitate the construction, training, and deployment of machine learning workloads on Kubernetes.

This instructor-led, live training session (available online or onsite) is tailored for entry-level to intermediate professionals seeking to develop robust ML workflows using Kubeflow.

Upon completion of this program, participants will acquire the competencies necessary to:

• Operate within the Kubeflow ecosystem and utilize its core components.
• Construct reproducible workflows leveraging Kubeflow Pipelines.
• Execute scalable training jobs on Kubernetes infrastructure.
• Serve machine learning models with efficiency using Kubeflow Serving capabilities.

Course Structure

• Structured presentations and collaborative strategic discussions.
• Practical laboratory exercises using authentic Kubeflow components.
• Applied tasks focused on developing end-to-end ML workflows.

Customization Options for the Course

• Tailored versions of this training can be configured to align with specific team technology stacks and project mandates for government operations.

This instructor-led, live training in US (delivered online or onsite) is designed for developers and data scientists aiming to build, deploy, and manage machine learning workflows on Kubernetes.

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

• Install and configure Kubeflow on-premises and in the cloud.
• Build, deploy, and manage ML workflows based on Docker containers and Kubernetes.
• Run entire machine learning pipelines on diverse architectures and cloud environments.
• Using Kubeflow to spawn and manage Jupyter notebooks.
• Build ML training, hyperparameter tuning, and serving workloads across multiple platforms.

This instructor-led, live training session US (delivered online or in person) is designed for software developers and DevOps practitioners with beginner to intermediate proficiency who seek to establish and administer a local Kubernetes environment utilizing Minikube. This curriculum is specifically tailored for government agencies requiring secure and efficient container orchestration capabilities.

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

• Installing and configuring Minikube on local workstations.
• Comprehending the fundamental principles and architectural framework of Kubernetes.
• Deploying and managing containers through kubectl commands and the Minikube dashboard interface.
• Implementing persistent storage and networking configurations for Kubernetes clusters.
• Leveraging Minikube to facilitate application development, testing, and debugging processes.

This instructor-led, live training in US (available online or onsite) is designed for intermediate-level developers and DevOps engineers seeking to incorporate Minikube into their development workflows. This course provides government agencies with essential skills for containerized application management.

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

• Establish and administer a local Kubernetes environment using Minikube.
• Deploy, manage, and troubleshoot applications within the Minikube platform.
• Incorporate Minikube into existing continuous integration and deployment pipelines.
• Enhance development efficiency by leveraging advanced Minikube capabilities.
• Implement industry-standard best practices for local Kubernetes development.

This instructor-led, live training in US (online or onsite) is designed for engineers who need to assess current methodologies and tools to make informed decisions regarding the adoption of MLOps practices within their agencies.

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

• Install and configure essential MLOps frameworks and utilities for government use cases.
• Assemble skilled teams capable of constructing and sustaining an effective MLOps infrastructure.
• Prepare, validate, and version data sets to ensure compatibility with machine learning models.
• Comprehend the essential components of an ML pipeline and identify the necessary tools for implementation.
• Evaluate various machine learning frameworks and servers suitable for production deployment in secure environments.
• Operationalize the end-to-end machine learning lifecycle to ensure reproducibility, maintainability, and accountability for government operations.