Kubeflow Certification Training Course Overview

Enroll for the 5-day Kubeflow Training course from Koenig Solutions.  Kubeflow is the open source machine learning toolkit on top of Kubernetes. Kubernetes is the industry standard for software delivery at scale and Kubeflow provides the cloud-native interface between K8s and data science tools - libraries, frameworks, pipelines, notebooks - bringing the Ops to ML.

Through a blend of hands-on labs and interactive lectures, you will learn how to build, deploy, and manage machine learning workflows on Kubernetes.

Target Audience:

  • Developers
  • Data scientists

Learning Objectives:

After completing this course, you will be able to:

  • Install and configure Kubeflow on premise and in the cloud using AWS EKS (Elastic Kubernetes Service).
  • Build, deploy, and manage ML workflows based on Docker containers and Kubernetes.
  • Run entire machine learning pipelines on diverse architectures and cloud environments.
  • Using Kubeflow to spawn and manage Jupyter notebooks.
  • Build ML training, hyperparameter tuning, and serving workloads across multiple platforms.

Kubeflow (40 Hours) Download Course Contents

Live Virtual Classroom Fee On Request
Group Training
01 - 05 Nov GTR 09:00 AM - 05:00 PM CST
(8 Hours/Day)

06 - 10 Dec GTR 09:00 AM - 05:00 PM CST
(8 Hours/Day)

1-on-1 Training (GTR)
4 Hours
8 Hours
Week Days
Weekend

Start Time : At any time

12 AM
12 PM

GTR=Guaranteed to Run
Classroom Training (Available: London, Dubai, India, Sydney, Vancouver)
Duration : On Request
Fee : On Request
On Request
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Course Modules

Module 1: Introduction to Kubernetes
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Module 4: Setting up a Cluster using AWS EKS
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Module 7: Data Storage Approaches
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Module 8: Creating a Kubeflow Pipeline
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Module 9: Triggering a Pipeline
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Module 10: Defining Output Artifacts
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Module 14: Multi-GPU Training
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Module 16: Working with JupyterHub
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Module 17: Networking and Load Balancing
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Module 18: Auto Scaling a Kubernetes Cluster
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Module 19: Troubleshooting
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Module 20: Summary and Conclusion
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Course Prerequisites
  • Familiarity with Python syntax 
  • Experience with Tensorflow, PyTorch, or other machine learning framework
  • An AWS account with necessary resources