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

This instructor-led, live training in Australia (online or onsite) is designed for advanced AI engineers and data scientists with intermediate to extensive experience who wish to enhance DeepSeek model performance, minimise latency, and deploy AI solutions efficiently using contemporary MLOps practices.

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

• Optimise DeepSeek models to enhance efficiency, accuracy, and scalability.
• Implement industry 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 Australia (online or onsite) is designed for advanced-level platform engineers and DevOps professionals who want 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, conducted in Australia (either online or on-site), is designed for Kubernetes practitioners aiming to prepare for the CKS exam.

By the conclusion of this training, participants will understand how to secure Kubernetes environments and container-based applications across the key stages of the application life cycle: 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.

This instructor-led, live training in Australia (online or onsite) is designed for engineers who want to use Helm to streamline the installation and management of 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.

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

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

• Install and configure Kubeflow in on-premise and cloud settings.
• Construct, deploy, and oversee ML workflows using Docker containers and Kubernetes.
• Run entire machine learning pipelines across diverse architectures and cloud environments.
• Use Kubeflow to spawn and manage Jupyter notebooks.
• Develop ML training, hyperparameter tuning, and serving workloads across multiple platforms.

This instructor-led, live training in Australia (online or onsite) is aimed at beginner-level to intermediate-level software developers and DevOps professionals who wish 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 Australia (online or onsite) is designed for intermediate-level developers and DevOps engineers who want to incorporate Minikube into their development workflow.

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

• Set up and manage a local Kubernetes environment using Minikube.
• Gain insight into deploying, managing, and debugging applications on Minikube.
• Integrate Minikube into their continuous integration and deployment pipelines.
• Enhance their development process by leveraging Minikube's advanced features.
• Apply best practices for local Kubernetes development.

This instructor-led, live training in Australia (online or onsite) is designed for engineers looking to assess the current approaches and tools available. This enables them to make informed decisions on how to adopt MLOps within their organisation.

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

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