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

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

• Create custom Docker images.
• Deploy and manage a high volume of Docker applications.
• Assess various container orchestration solutions and select the most appropriate option.
• Establish a continuous integration pipeline for Docker applications.
• Integrate Docker applications with existing continuous integration workflows.
• Enhance the security of Docker applications.
• Leverage Kubernetes to deploy and manage multiple environments within a single cluster.
• Secure, scale, and monitor Kubernetes clusters.

This instructor-led, live training in Serbia (online or onsite) targets advanced-level AI engineers and data scientists with intermediate-to-advanced experience who aim to enhance DeepSeek model performance, reduce latency, and deploy AI solutions efficiently using modern MLOps practices.

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

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

This instructor-led, live training in Serbia (online or onsite) is designed for advanced-level platform engineers and DevOps professionals who want to master application scaling using microservices and Kubernetes.

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

• Design and implement scalable microservices architectures.
• Deploy and manage applications on Kubernetes clusters.
• Use Helm charts for efficient service deployment.
• Monitor and maintain the health of microservices in production.
• Apply best practices for security and compliance in a Kubernetes environment.

Kubernetes is a widely adopted container orchestration platform designed to manage distributed applications at scale.

This instructor-led live training (available online or onsite) targets advanced practitioners seeking to leverage AI and machine learning techniques to optimize Kubernetes resource usage, scheduling decisions, and autoscaling strategies.

Upon completing this program, participants will be able to:

• Apply AI/ML models to enhance workload scheduling decisions within Kubernetes.
• Utilize predictive analytics to optimize CPU, GPU, and memory allocation.
• Implement intelligent autoscaling mechanisms using reinforcement learning and metric forecasting.
• Reduce infrastructure costs and latency through automated resource optimization.

Format of the Course

• Instructor-guided technical presentations and deep-dive discussions.
• Hands-on lab work using real Kubernetes clusters.
• Practical exercises applying AI models to real operational scenarios.

Course Customization Options

• To tailor this course to your specific platform setup or operational requirements, please contact us for customization.

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

Today, Kubernetes stands as a 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 helping our customers validate their performance in Kubernetes environments by preparing them and encouraging them to 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, so we recommend participation 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, training is also focused on gaining practical experience in Kubernetes application development, so we recommend taking part in it, even if you don't intend to take CKAD exam.

NobleProg have been delivering Docker & Kubernetes training from 2015. With more than 360 successfully completed training projects, we became one of the best known training company worldwide in field of containerization. Since 2019 we are also helping our customers to confirm their performance in k8s environment by preparing them and encouraging to pass CKA and CKAD exams.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.
• To learn more about CKAD, please visit: https://training.linuxfoundation.org/certification/certified-kubernetes-application-developer-ckad/

This instructor-led, live training in Serbia (online or onsite) is designed for Kubernetes practitioners seeking to prepare for the CKS exam.

By the end of this training, participants will understand how to secure Kubernetes environments and container-based applications throughout the various stages of an application's lifecycle: build, deployment, and runtime.

This 7-day programme provides a comprehensive, hands-on journey into designing, deploying, and operating cloud-native applications using modern DevOps practices.

Participants will explore how to design scalable microservices architectures, optimise container environments, and manage production workloads using Kubernetes. The course covers advanced deployment strategies, GitOps-based automation, and observability practices to ensure system reliability and performance.

A strong focus is placed on real-world operational challenges, including incident response, failure simulation, and root cause analysis. The programme concludes with the use of AI-powered tools to support troubleshooting and accelerate operational decision-making.

By the end of the training, participants will have a clear understanding of how to build, deploy, monitor, and maintain resilient distributed systems in a Kubernetes-based environment.

Docker serves as a containerization platform designed to create reproducible, portable, and scalable environments for machine learning systems.

This instructor-led, live training (available online or onsite) targets intermediate to advanced technical professionals who aim to containerize and operationalize complete ML pipelines using Docker.

Upon completing this training, participants will be able to:

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

Format of the Course

• Interactive lectures complemented by practical demonstrations.
• Hands-on exercises centered on building real-world ML pipeline components.
• Live-lab implementation for end-to-end containerized workflows.

Course Customization Options

• For customized training aligned with specific ML infrastructure needs, please contact us to discuss options.

This instructor-led, live training in Serbia (online or onsite) is designed for engineers looking to utilize Helm to simplify the installation and management of Kubernetes applications.

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

• Install and configure Helm.
• Build reproducible Kubernetes application deployments.
• Distribute applications as Helm charts.
• Execute third-party applications packaged as Helm charts.
• Manage releases of Helm packages.

Kubeflow is an open-source platform designed to streamline building, training, and deploying machine learning workloads on Kubernetes.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level professionals who wish to build reliable ML workflows using Kubeflow.

Upon completion of this training, attendees will gain the skills to:

• Navigate the Kubeflow ecosystem and core components.
• Build reproducible workflows with Kubeflow Pipelines.
• Run scalable training jobs on Kubernetes.
• Serve machine learning models efficiently using Kubeflow Serving.

Format of the Course

• Guided presentations and collaborative discussions.
• Hands-on labs with real Kubeflow components.
• Practical exercises to build end-to-end ML workflows.

Course Customization Options

• Customized versions of this training can be arranged to align with your team’s technology stack and project requirements.

This instructor-led, live training in Serbia (online or onsite) targets beginner to intermediate software developers and DevOps professionals who want to learn how to set up and manage a local Kubernetes environment using Minikube.

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

• Install and configure Minikube on their local machine.
• Grasp the fundamental concepts and architecture of Kubernetes.
• Deploy and manage containers using kubectl and the Minikube dashboard.
• Configure persistent storage and networking solutions for Kubernetes.
• Leverage Minikube for application development, testing, and debugging.

This instructor-led, live training in Serbia (online or onsite) is designed for intermediate-level developers and DevOps engineers who aim to use Minikube as part of their development workflow.

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

• Set up and manage a local Kubernetes environment using Minikube.
• Understand how to deploy, manage, and debug applications on Minikube.
• Integrate Minikube into their continuous integration and deployment pipelines.
• Optimize their development process using Minikube's advanced features.
• Apply best practices for local Kubernetes development.

This instructor-led, live training in Serbia (online or onsite) is aimed at engineers who wish to evaluate the approaches and tools available today to make an intelligent decision on the path forward in adopting MLOps within their organization.

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

• Install and configure various MLOps frameworks and tools.
• Assemble the right kind of team with the right skills for constructing and supporting an MLOps system.
• Prepare, validate and version data for use by ML models.
• Understand the components of an ML Pipeline and the tools needed to build one.
• Experiment with different machine learning frameworks and servers for deploying to production.
• Operationalize the entire Machine Learning process so that it's reproduceable and maintainable.