Kubeflow Training in Christchurch

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.

Course Format

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

Course Customisation Options

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

This instructor-led, live training in Christchurch (available online or onsite) is tailored for developers and data scientists who wish 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 both on-premises and in the cloud using AWS EKS (Elastic Kubernetes Service).
• Build, deploy, and manage ML workflows based on Docker containers and Kubernetes.
• Execute complete 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 Christchurch (online or on-site) is tailored for engineers who wish to deploy Machine Learning workloads to an AWS EC2 server.

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

• Install and configure Kubernetes, Kubeflow, and other required software on AWS.
• Utilise EKS (Elastic Kubernetes Service) to simplify the process of initialising a Kubernetes cluster on AWS.
• Create and deploy a Kubernetes pipeline to automate and manage ML models in production.
• Train and deploy TensorFlow ML models across multiple GPUs and machines operating in parallel.
• Leverage other AWS managed services to extend an ML application.

This instructor-led, live training in Christchurch (online or on-site) is designed for engineers who wish to deploy Machine Learning workloads to the Azure cloud.

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

• Install and configure Kubernetes, Kubeflow, and other required software on Azure.
• Use Azure Kubernetes Service (AKS) to simplify the process of initialising a Kubernetes cluster on Azure.
• Create and deploy a Kubernetes pipeline for automating and managing ML models in production.
• Train and deploy TensorFlow ML models across multiple GPUs and machines running in parallel.
• Leverage other AWS managed services to extend an ML application.

This instructor-led, live training in Christchurch (online or on-site) is aimed at developers and data scientists who wish 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 both on-premises and in the cloud.
• Build, deploy, and manage ML workflows using Docker containers and Kubernetes.
• Execute complete machine learning pipelines across diverse architectures and cloud environments.
• Use Kubeflow to launch and manage Jupyter notebooks.
• Construct ML training, hyperparameter tuning, and serving workloads across multiple platforms.