Kubeflow Training

• Professional Background: No prior experience with Kubeflow is required; however, a basic understanding of cloud computing, Kubernetes, and machine learning will enrich your learning experience. 

• Technical Proficiency: Learners should have a strong foundation in programming, particularly Python, as well as basic knowledge of machine learning concepts. 

• Interest in Data Science and ML: This course is ideal for individuals seeking to expand their skills in machine learning operations (MLOps) and improve their understanding of workflow management in cloud-native environments. 

• Master Kubeflow Pipelines: Gain the ability to build and manage end-to-end machine learning pipelines on Kubernetes using Kubeflow, enhancing your workflow automation and model deployment processes. 

• Enhance Model Deployment and Management: Learn how to deploy, scale, and monitor machine learning models effectively, enabling you to streamline production-ready model workflows. 

• Improve Collaboration Across Teams: Develop strategies to foster collaboration between data scientists, machine learning engineers, and DevOps teams, enhancing the productivity of cross-functional teams. 

• Navigate Cloud-Based ML Environments: Understand how to leverage Kubeflow in cloud-native environments, taking advantage of scalable infrastructure and resources for machine learning projects. 

The Kubeflow Training Course is designed for professionals who want to enhance their skills in machine learning operations and work effectively with cloud-native tools for deploying models. Below are the individuals who can benefit from this course:

• Data Scientists
• Machine Learning Engineers
• DevOps Engineers
• Cloud Architects
• IT Professionals
• Data Engineers
• AI/ML Research Professionals
• Kubernetes Engineers
• MLOps Specialists 

Module 1: Getting Started 

• Introduction 

• Architecture 

• Installing Kubeflow 
   

Module 2: Central Dashboard 

• Introduction to Central Dashboard 

• Customising Menu Items 

• Registration Flow 
   

Module 3: Kubeflow Notebooks 

• Overview 

• Container Images 

• Submit Kubernetes Resources 

• Troubleshooting 

• Kubeflow Notebooks API 
   

Module 4: Kubeflow Pipelines 

• Introduction 

• Overview 

• Concepts Used in Pipelines 

• Installation 

• Pipelines SDK 

• Pipelines SDK (v2) 

• Troubleshooting 
   

Module 5: Katib 

• Introduction to Katib 

• Getting Started with Katib 

• Running an Experiment 

• Overview of Trial Templates 

• Using Early Stopping 

• Katib Configuration Overview 

• Environment Variables for Katib Components 
   

Module 6: Multi-Tenancy 

• Introduction to Multi-User Isolation 

• Design for Multi-User Isolation 

• Getting Started with Multi-User Isolation 
   

Module 7: External Add-Ons 

• Elyra 

• Istio 

• Kale 

• KServe 

• Migration 

• Models UI 

• Run Your First InferenceService 

• Fairing 

• Overview of Kubeflow Fairing 

• Install Kubeflow Fairing 

• Configure Kubeflow Fairing 

• Fairing on Azure and GCP 

• Feature Store 

• Introduction to Feast 

• Getting Started with Feast 

• Tools for Serving 

• Seldon Core Serving 

• BentoML 

• MLRun Serving Pipelines 

• NVIDIA Triton Inference Server 

• TensorFlow Serving 

• TensorFlow Batch Prediction 
   

Module 8: Kubeflow Distributions 

• Kubeflow on AWS 

• Arrikto Enterprise Kubeflow 

• Arrikto Kubeflow as a Service 

• Charmed Kubeflow 
   

Module 9: Kubeflow on Azure 

• Deployment 

• Authentication Using OIDC in Azure 

• Azure Machine Learning Components 

• Access Control for Azure Deployment 

• Configure Azure MySQL Database to Store Metadata 

• Troubleshooting Deployments on Azure AKS 
   

Module 10: Kubeflow on Google Cloud 

• Deployment 

• Pipelines on Google Cloud 

• Customise Kubeflow on GKE 

• Using Your Own Domain 

• Authenticating Kubeflow to Google Cloud 

• Securing Your Clusters 

• Troubleshooting Deployments on GKE 

• Kubeflow On-Premises on Anthos 
   

Module 11: Kubeflow on IBM Cloud 

• Create or Access an IBM Cloud Kubernetes Cluster 

• Create or Access an IBM Cloud Kubernetes Cluster on a VPC 

• Kubeflow Deployment on IBM Cloud 

• Pipelines on IBM Cloud Kubernetes Service (IKS) 

• Using IBM Cloud Container Registry (ICR) 

• End-to-End Kubeflow on IBM Cloud 
   

Module 12: Kubeflow on Nutanix Karbon 

• Install Kubeflow on Nutanix Karbon 

• Integrate with Nutanix Storage 

• Uninstall Kubeflow 
   

Module 13: Kubeflow Operator 

• Introduction to Kubeflow Operator 

• Installing Kubeflow Operator 

• Installing Kubeflow 

• Uninstalling Kubeflow 

• Uninstalling Kubeflow Operator 

• Troubleshooting 
   

Module 14: Kubeflow on OpenShift 

• Install Kubeflow on OpenShift 

• Uninstall Kubeflow