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Kubeflow Training Overview
Kubeflow Training Course Outline
Module 1: Getting Started
- Introduction
- Architecture
- Installing Kubeflow
Module 2: Central Dashboard
- Introduction to Central Dashboard
- Customizing 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
- Customize 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
Who should attend this Kubeflow Training Course?
The Kubeflow Course in the United States is designed for those who want to get better at streamlining their Machine Learning Workflows via Kubeflow, an open-source Machine Learning platform. This course can be beneficial to variety of professionals, including:
- Data Scientists
- Software Developers
- Data Analysts
- Data Engineers
- DevOps Engineers
- Cloud Engineers
- AI and ML Experts
Prerequisites of the Kubeflow Training Course
There are no formal prerequisites for this Kubeflow Course.
Kubeflow Training Course Overview
Kubeflow is an essential platform for orchestrating and deploying Machine Learning (ML) and data science workflows on Kubernetes. In the rapidly evolving field of DevOps, mastering Kubeflow is crucial. Kubeflow streamlines the deployment of ML models, making it pertinent for DevOps professionals looking to enhance their skills in ML operations. This course in the United States equips learners with the knowledge to excel in DevOps by integrating Machine Learning seamlessly.
Proficiency in Kubeflow is imperative for DevOps professionals aiming to excel in their careers. It empowers them to manage complex ML pipelines efficiently, ensuring the seamless integration of ML models into their applications. This DevOps Certification Training in the United States is tailored for DevOps Practitioners, Data Engineers, and anyone seeking DevOps Certifications, as it equips them with the skills needed to navigate the increasingly data-driven world of DevOps.
In this 2-day Kubeflow Training offered by The Knowledge Academy in the United States, delegates will gain a deep understanding of Kubeflow and the development of Machine Learning pipelines. During this DevOps Certification, delegates will learn about the architecture and installation process of Kubeflow. Our highly professional instructors with years of experience in teaching technical DevOps Courses will conduct this training course.
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 authorization 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
After completing this DevOps Certification Course in the United States, delegates will be equipped with the knowledge and skills needed to excel in DevOps roles requiring ML integration. This course serves as a solid foundation for those pursuing DevOps Certification, helping them stand out in the competitive field of DevOps.
What’s included in this Kubeflow Training Course?
- World-Class Training Sessions from Experienced Instructors
- Kubeflow Certificate
- Digital Delegate Pack
Why choose us
Ways to take this course
Experience live, interactive learning from home with The Knowledge Academy's Online Instructor-led Kubeflow Training. Engage directly with expert instructors, mirroring the classroom schedule for a comprehensive learning journey. Enjoy the convenience of virtual learning without compromising on the quality of interaction.
Unlock your potential with The Knowledge Academy's Kubeflow Training, accessible anytime, anywhere on any device. Enjoy 90 days of online course access, extendable upon request, and benefit from the support of our expert trainers. Elevate your skills at your own pace with our Online Self-paced sessions.
What our customers are saying
Kubeflow Training FAQs
Kubeflow is an open-source platform designed to make it easy to deploy, manage, and scale Machine Learning (ML) workflows on Kubernetes.
There are no formal prerequisites for this Kubeflow Training Course.
This course is ideal for Data Scientists, Machine Learning Engineers, Software Developers, and IT professionals interested in leveraging Kubernetes for managing and scaling ML workflows.
The goal of Kubeflow is to provide a comprehensive platform that streamlines the process of deploying, managing, and scaling Machine Learning models in production environments using Kubernetes.
Some benefits of Kubeflow include increased productivity, scalability, portability, and reproducibility of Machine Learning workflows, along with better utilisation of resources and easier collaboration among team members.
The Kubeflow pipeline is designed to orchestrate and automate complex machine learning workflows, enabling users to create, deploy, and manage end-to-end ML pipelines efficiently.
In this course, participants typically learn how to set up and configure Kubeflow, build and deploy machine learning models, create and manage pipelines, and troubleshoot common issues.
Companies across various industries, including technology, finance, healthcare, and retail, actively recruit professionals with certified Kubeflow Training, seeking expertise in scalable ML deployment and management.
Kubeflow Training is gaining popularity in the United States as organisations increasingly adopt Kubernetes for managing their Machine Learning workloads and seek professionals with expertise in Kubeflow.
KATIB is a hyperparameter tuning framework integrated with Kubeflow, which automates the process of optimising ML model parameters to improve performance and accuracy.
Securing a cluster using Kubeflow involves implementing best practices such as Role-Based Access Control (RBAC), network policies, encryption, and authentication mechanisms to protect sensitive data and resources.
Some ways to troubleshoot using Kubeflow include checking logs, monitoring resource usage, inspecting pod status, verifying network configurations, and debugging code errors in ML pipelines.
In this course, participants typically gain access to tools such as Jupyter Notebooks, TensorFlow, PyTorch, Apache Spark, and other ML frameworks and libraries integrated with the Kubeflow platform.
Some operators in Kubeflow include TensorFlow Operator, PyTorch Operator, Katib Hyperparameter Tuning Operator, and Seldon Core for deploying and managing ML workloads on Kubernetes.
Multi-user isolation in Kubeflow ensures that each user or team has their own isolated environment within the Kubernetes cluster, preventing interference or access to each other's resources and data.
Fundamentals of Kubeflow include its integration with Kubernetes, support for scalable and portable ML workflows, automation of model deployment and management, and extensibility through custom components and operators.
Organisations benefit from Kubeflow by streamlining their ML workflow management, improving collaboration among data scientists and developers, reducing time-to-market for ML applications, and achieving greater efficiency and scalability in model deployment.
TensorFlow is a popular open-source ML framework, while Kubeflow is a platform for managing ML workflows on Kubernetes. While TensorFlow focuses on model development and training, Kubeflow provides tools for deploying, scaling, and managing TensorFlow models in production environments.
To run a Kubeflow pipeline, users typically define their ML workflow using the Kubeflow Pipelines SDK, compile the pipeline into a Kubeflow-compatible format, and submit it to the Kubeflow Pipelines dashboard or API for execution on a Kubernetes cluster.
The training fees for Kubeflow Training certification in the United States starts from $2295
The Knowledge Academy is the Leading global training provider for Kubeflow Training.
Please see our DevOps Certification courses available in the United States
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Resources are included for a comprehensive learning experience.
"Really good course and well organised. Trainer was great with a sense of humour - his experience allowed a free flowing course, structured to help you gain as much information & relevant experience whilst helping prepare you for the exam"
Joshua Davies, Thames Water
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