Kubeflow Course Overview

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.