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Kubeflow Training
What is Kubeflow Training?
Kubeflow is an essential platform for orchestrating and deploying Machine Learning (ML) and data science workflows on Kubernetes. In the evolving field of DevOps, mastering Kubeflow is crucial for streamlining the deployment of ML models. It helps integrate ML into DevOps practices, making it highly relevant for professionals looking to enhance their capabilities in ML operations.
Proficiency in Kubeflow is vital for DevOps professionals aiming to excel in their careers. It enables them to manage complex ML pipelines efficiently, ensuring seamless integration of ML models into applications. This course is ideal for DevOps Practitioners, Data Engineers, and IT professionals seeking to boost their skills and navigate the increasingly data-driven landscape of DevOps.
This 2-day Kubeflow Training by Oakwood International provides delegates with in-depth knowledge of Kubeflow and the development of ML pipelines. Delegates will learn the architecture, installation, and use of Kubeflow’s central dashboard for managing ML workflows. Delivered by experienced instructors, the training offers practical insights, empowering delegates to deploy and manage ML systems effectively in DevOps environments.
Course Objectives:
- To deploy Machine Learning systems to several environments for development
- To evaluate the output of many stages of the Machine Learning workflow
- To use Jupyter and TensorFlow in Kubeflow Notebooks effectively
- To set up Kubeflow with authentication and authorisation support through OIDC in Azure
- To identify the problems and collect data to train the Machine Learning model
- To evaluate the output of various stages and apply changes to the model
Upon completing this course, delegates will have the skills to excel in DevOps roles requiring ML integration. This training serves as a solid foundation for pursuing DevOps Certification, helping delegates stand out in the competitive DevOps field.
Course Outline
Kubeflow Training
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
Included
Included
- No course includes are available.
Offered In This Course:
-
Video Content
-
eLearning Materials
-
Study Resources
-
Completion Certificate
-
Tutor Support
-
Interactive Quizzes
Individual Training
Individual Training fosters personal growth, enhances professional skills, and builds confidence.
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Corporate Training
Corporate Training improves employee skills, increases productivity, and aligns teams with company objectives.
Learning Options
Discover a range of flexible learning options designed to meet your needs. Select the format that best supports your personal growth and goals.
Online Instructor-Led Training
- Live virtual classes led by experienced trainers, offering real-time interaction and guidance for optimal learning outcomes.
Online Self-Paced Training
- Flexible learning at your own pace, with access to comprehensive course materials and resources available anytime, anywhere.
Build your future with Oakwood International
We empower you with the skills, knowledge, and confidence to excel in your career. Join us and take the first step towards realising your professional goals.
Frequently Asked Questions
Q. What is the focus of the Kubeflow Training Course?
The course
focuses on deploying, managing, and scaling Machine Learning workflows using
Kubeflow, providing practical knowledge to streamline ML processes in
Kubernetes environments.
Q. Who will benefit from this course?
DevOps
Practitioners, Data Engineers, and IT professionals interested in improving their
ability to deploy and manage ML models efficiently will benefit from this
course.
Q. What skills will I gain from this training?
Delegates will
learn to deploy ML systems, use Kubeflow’s components effectively, and automate
the ML lifecycle for improved productivity and efficiency.
Q. How is the training delivered?
The training is
delivered through interactive sessions that combine theoretical knowledge,
hands-on exercises, and real-world case studies to provide practical
understanding.
Q. What are the career benefits of this certification?
This
certification will boost your ability to manage Machine Learning workflows in
DevOps environments, enhancing your career prospects in data engineering, ML
operations, and DevOps roles.
