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

Upon completion 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 one.
• Establish a continuous integration workflow for Docker applications.
• Integrate Docker applications into existing continuous integration toolchains.
• Enhance the security of Docker applications.
• Utilize Kubernetes to deploy and manage diverse environments within a single cluster.
• Secure, scale, and monitor a Kubernetes cluster.

This instructor-led, live training in Italy (online or onsite) is designed for advanced-level AI engineers and data scientists with intermediate to advanced experience who aim to improve DeepSeek model performance, reduce latency, and efficiently deploy AI solutions using contemporary MLOps practices.

Upon completion 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 both cloud and on-premise infrastructure.
• Monitor, maintain, and scale AI solutions effectively.

This instructor-led, live training in Italy (online or onsite) targets advanced-level platform engineers and DevOps professionals who wish 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.
• Utilize 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 serves as a widely adopted container orchestration platform, enabling the management of distributed applications at scale.

This live training program, delivered by an instructor either online or onsite, is designed for advanced practitioners looking to leverage artificial intelligence and machine learning to optimize Kubernetes resource allocation, scheduling decisions, and autoscaling strategies.

Upon completing this course, participants will be able to:

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

Course Format

• Instructor-led technical presentations alongside in-depth discussions.
• Practical lab exercises conducted on live Kubernetes clusters.
• Hands-on application of AI models to real-world operational scenarios.

Customization Options

• To adapt this course to your specific platform architecture or operational needs, please reach out to us for customization details.

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

Kubernetes has become a leading platform for container orchestration.

Since 2015, NobleProg has been providing Docker and Kubernetes training. With over 360 successfully completed training projects, we have established ourselves as one of the most recognized training providers globally in the field of containerization.

Since 2019, we have also been supporting our customers in validating their skills in k8s environments by preparing them and encouraging them to take 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.

Moreover, the training focuses on gaining practical experience in Kubernetes Administration, so we recommend participating even if you do not plan to take the CKA exam.

Course Format

• Interactive lectures and discussions.
• Abundant exercises and practice sessions.
• 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) certification program was created by The Linux Foundation and the Cloud Native Computing Foundation (CNCF), the organization responsible for hosting Kubernetes.

This instructor-led, live training (available online or in-person) is designed for developers looking to validate their ability to design, build, configure, and expose cloud-native applications on Kubernetes.

Additionally, the training emphasizes gaining practical experience in Kubernetes application development. Therefore, we recommend participating even if you do not plan to take the CKAD exam.

NobleProg has been providing Docker and Kubernetes training since 2015. With over 360 successfully completed training projects, we have become one of the most renowned training providers globally in the field of containerization. Since 2019, we have also been assisting our customers in validating their performance in Kubernetes environments by preparing them to pass the CKA and CKAD exams.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practice sessions.
• 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 CKAD, please visit: https://training.linuxfoundation.org/certification/certified-kubernetes-application-developer-ckad/

This instructor-led, live training in Italy (online or onsite) is aimed at Kubernetes practitioners who wish to prepare for the CKS exam.

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

This intensive 7-day programme offers a thorough, practical exploration into the design, deployment, and operation of cloud-native applications through contemporary DevOps methodologies.

Attendees will delve into crafting scalable microservices architectures, optimizing containerized environments, and managing production workloads via Kubernetes. The curriculum addresses advanced deployment techniques, GitOps-driven automation, and observability strategies to guarantee system reliability and optimal performance.

The programme places significant emphasis on tackling real-world operational hurdles, such as incident management, failure simulation, and root cause analysis. It concludes with the integration of AI-driven tools to streamline troubleshooting and expedite operational decisions.

Upon completion, participants will possess a solid understanding of how to construct, deploy, monitor, and sustain resilient distributed systems within a Kubernetes ecosystem.

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

This instructor-led live training, available both online and onsite, targets intermediate to advanced technical professionals seeking 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 utilizing Docker and complementary tools.
• Implement versioning, reproducibility, and CI/CD practices for ML components.
• Deploy, monitor, and scale ML services within containerized environments.

Course Format

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

Customization Options

• For training tailored to specific ML infrastructure requirements, please contact us to explore available options.

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

Upon completion 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 simplify the process of building, training, and deploying machine learning workloads on Kubernetes.

This instructor-led live training (available online or onsite) is designed for professionals at beginner to intermediate levels who aim to establish robust ML workflows using Kubeflow.

After completing this training, participants will acquire the ability to:

• Explore the Kubeflow ecosystem and its fundamental components.
• Develop reproducible workflows utilizing Kubeflow Pipelines.
• Execute scalable training jobs on Kubernetes.
• Deploy machine learning models efficiently with Kubeflow Serving.

Course Format

• Structured presentations coupled with collaborative discussions.
• Practical labs involving real-world Kubeflow components.
• Hands-on exercises to construct end-to-end ML workflows.

Course Customization Options

• Tailored versions of this training can be configured to match your team’s technology stack and specific project needs.

This instructor-led, live training in Italy (online or onsite) is tailored for software developers and DevOps professionals at beginner to intermediate levels 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.
• Understand the basic concepts and architecture of Kubernetes.
• Deploy and manage containers using kubectl and the Minikube dashboard.
• Set up persistent storage and networking solutions for Kubernetes.
• Utilize Minikube for developing, testing, and debugging applications.

This instructor-led, live training in Italy (online or onsite) is designed for intermediate developers and DevOps engineers aiming to utilize Minikube within 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 Italy (online or onsite) is designed for engineers who want to assess the current approaches and tools available, enabling them to make informed decisions about adopting MLOps within their organizations.

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

• Install and configure various MLOps frameworks and tools.
• Build a team with the appropriate skills to construct and support an MLOps system.
• Prepare, validate, and version data for use by ML models.
• Understand the components of an ML Pipeline and the tools required to build one.
• Experiment with different machine learning frameworks and servers for production deployment.
• Operationalize the entire Machine Learning process to ensure it is reproducible and maintainable.