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Kubeflow Course Overview

Kubeflow Course Overview

The Kubeflow course is a comprehensive training program designed to equip learners with the skills necessary to deploy, manage, and scale machine learning workflows using Kubeflow on Kubernetes. Whether the learners are interested in Kubeflow training for personal advancement or professional development, the course covers a wide range of topics, from the basics of Kubernetes to the sophisticated techniques of Kubeflow distributed training.

Starting with an introduction to Kubernetes, the course lays the foundation needed to understand Kubeflow's interaction with container orchestration. As the course progresses, learners will explore Kubeflow's features and architecture, and how it can be implemented on AWS, on-premise, and on other public cloud providers. Practical modules guide students through setting up clusters, creating and managing Kubeflow pipelines, hyperparameter tuning with TensorFlow, and scaling with multi-GPU training.

By understanding data storage approaches, creating inference servers, and utilizing JupyterHub within the Kubeflow ecosystem, learners will gain hands-on experience. The course also addresses critical operational skills, such as networking, load balancing, auto-scaling, and troubleshooting. Completing the course will empower students with a solid understanding of Kubeflow, positioning them to effectively deploy machine learning workflows at scale.

This is a Rare Course and it can be take up to 3 weeks to arrange the training.

4.6 Ratings

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4-Hour Sessions

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  • Live Online Training (Duration : 40 Hours)
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Classroom Training price is on request

  • Live Online Training (Duration : 40 Hours)
  • Per Participant

♱ Excluding VAT/GST

Classroom Training price is on request

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Course Prerequisites

To ensure that you are well-prepared to take full advantage of our comprehensive Kubeflow course, the following minimum prerequisites are recommended:


  • Basic understanding of containerization technologies, particularly Docker.
  • Familiarity with Kubernetes concepts such as Pods, Deployments, Services, and basic cluster management operations.
  • Experience with using command-line interfaces (CLI) in a Linux environment.
  • Fundamental knowledge of cloud computing and the services offered by cloud providers, especially if you're interested in deploying Kubeflow on cloud platforms like AWS.
  • Basic understanding of Machine Learning concepts and workflows would be beneficial, though not mandatory.
  • Some programming experience, preferably in Python, to follow along with Kubeflow Pipelines and other code examples presented in the course.

These prerequisites are designed to ensure that you can effectively engage with the course content and participate in hands-on exercises. However, individuals with a strong willingness to learn and a commitment to self-study have successfully completed our courses starting with various levels of initial knowledge.


Target Audience for Kubeflow

  1. Koenig Solutions' Kubeflow course is designed for IT professionals seeking to leverage Kubernetes for machine learning workflows.


  • DevOps Engineers
  • Machine Learning Engineers
  • Data Scientists
  • IT Professionals with a focus on Kubernetes
  • Cloud Architects
  • Software Engineers interested in ML Ops
  • System Administrators aiming to manage ML infrastructure
  • AI/ML Consultants
  • Technical Project Managers overseeing ML projects
  • Infrastructure Engineers looking to deploy scalable ML models
  • Technical Leads coordinating cross-functional DevOps and ML teams


Learning Objectives - What you will Learn in this Kubeflow?

  1. This Kubeflow course by Koenig Solutions aims to equip learners with practical skills to deploy, manage, and scale machine learning workflows using Kubeflow on Kubernetes.

  2. Learning Objectives and Outcomes:

  • Understand the fundamentals of Kubernetes, the platform on which Kubeflow operates.
  • Gain an overview of Kubeflow's features and architecture to leverage its full potential for machine learning workflows.
  • Compare Kubeflow deployment options on AWS, on-premise, and other public cloud providers to make informed decisions.
  • Learn to set up a Kubernetes cluster using AWS EKS and on-premise using Microk8s to meet specific organizational requirements.
  • Master deploying Kubernetes clusters with a GitOps approach for efficient operations and version control.
  • Explore data storage strategies for machine learning models and datasets within Kubernetes environments.
  • Create and trigger Kubeflow pipelines for automating machine learning workflows and managing complex processes.
  • Define and manage output artifacts to ensure traceability and reproducibility in machine learning experiments.
  • Perform hyperparameter tuning with TensorFlow to optimize machine learning models.
  • Utilize Multi-GPU training for scaling machine learning computations and reducing training time.