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

This instructor-led, live training in Taiwan (online or onsite) targets advanced-level AI engineers and data scientists with intermediate-to-advanced expertise 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 enhanced 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 Taiwan (online or onsite) is aimed at advanced-level platform engineers and DevOps professionals who wish to master scaling applications 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.

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

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

This 7-day programme offers 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.

This instructor-led, live training in Taiwan (online or onsite) is designed for engineers who want 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.
• Distribute applications as Helm charts.
• Run third-party applications packaged as Helm charts.
• Manage releases of Helm packages.

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

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

• Install and configure Kubeflow in on-premise and cloud-based settings.
• Develop, deploy, and manage ML workflows utilizing Docker containers and Kubernetes.
• Execute complete machine learning pipelines across various architectures and cloud environments.
• Utilize Kubeflow to generate and administer Jupyter notebooks.
• Create ML training, hyperparameter tuning, and serving workloads spanning multiple platforms.

This instructor-led, live training in Taiwan (online or onsite) is designed for 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.
• 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 Taiwan (online or onsite) is aimed at intermediate-level developers and DevOps engineers who wish to use Minikube as a 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 Taiwan (online or onsite) is designed for engineers who wish to evaluate the current approaches and tools to make informed decisions regarding the adoption of MLOps within their organization.

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

• Install and configure various MLOps frameworks and tools.
• Assemble a team with the appropriate 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 reproducible and maintainable.