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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
- 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
Who should attend this Kubeflow Training Course?
The Kubeflow Training Course is designed for those who want to get better at streamlining their Machine Learning Workflows via Kubeflow, an open-source Machine Learning platform. This DevOps Certification 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 Training 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 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 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, 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. They will also learn about the central dashboard that provides quick access to Kubeflow components deployed in a cluster. Our highly professional instructors with years of experience in teaching technical 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 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
After completing this DevOps Certification Course, 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.
Live classes
Join a scheduled class with a live instructor and other delegates.
Interactive
Engage in activities, and communicate with your trainer and peers.
Global Pool of the Best Trainers
We handpick from a global pool of expert trainers for our Online Instructor-led courses.
Expertise
With 10+ years of quality, instructor-led training, we equip professionals with lasting skills for success.
Global Reach
With classes running in all timezones, access any of our courses and course material from anywhere, anytime.
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.
Experience the most sought-after learning style with The Knowledge Academy's Kubeflow Training. Available in 490+ locations across 190+ countries, our hand-picked Classroom venues offer an invaluable human touch. Immerse yourself in a comprehensive, interactive experience with our expert-led Kubeflow Training sessions.
Highly experienced trainers
Boost your skills with our expert trainers, boasting 10+ years of real-world experience, ensuring an engaging and informative training experience
State of the art training venues
We only use the highest standard of learning facilities to make sure your experience is as comfortable and distraction-free as possible
Small class sizes
Our Classroom courses with limited class sizes foster discussions and provide a personalised, interactive learning environment
Great value for money
Achieve certification without breaking the bank. Find a lower price elsewhere? We'll match it to guarantee you the best value
Streamline large-scale training requirements with The Knowledge Academy’s In-house/Onsite Kubeflow Training at your business premises. Experience expert-led classroom learning from the comfort of your workplace and engage professional development.
Tailored learning experience
Leverage benefits offered from a certification that fits your unique business or project needs
Maximise your training budget
Cut unnecessary costs and focus your entire budget on what really matters, the training.
Team building opportunity
Our Kubeflow Training offers a unique chance for your team to bond and engage in discussions, enriching the learning experience beyond traditional classroom settings
Monitor employees progress
The course know-how will help you track and evaluate your employees' progression and performance with relative ease
What our customers are saying
Kubeflow Training FAQs
Kubeflow is an open-source platform designed for deploying, managing, and scaling machine learning workflows on Kubernetes, streamlining the machine learning lifecycle from experimentation to production.
Delegates should have a fundamental understanding of machine learning concepts, familiarity with Kubernetes, and experience in programming, ideally with Python, to effectively engage with the course material.
This course is ideal for data scientists, machine learning engineers, DevOps professionals, and IT practitioners interested in leveraging Kubernetes for machine learning workflows and enhancing their skill set.
The primary goal of Kubeflow is to simplify and accelerate the deployment of machine learning models, providing tools and frameworks that promote scalability, reproducibility, and collaboration.
Kubeflow enables easier management of machine learning models, efficient resource utilisation, seamless integration with Kubernetes, and enhanced collaboration among data scientists, fostering a robust machine learning environment.
Kubeflow pipelines provide a framework for building, deploying, and managing end-to-end machine learning workflows, allowing users to automate and monitor experiments while ensuring reproducibility.
Participants will learn how to deploy Kubeflow on Kubernetes, manage pipelines, utilise various components like Katib and training operators, and implement best practices for machine learning workflows.
Numerous technology companies, cloud service providers, and enterprises leveraging machine learning actively seek Kubeflow-certified professionals, recognising the growing demand for expertise in cloud-native machine learning solutions.
Kubeflow training is gaining traction in the United Kingdom as organisations increasingly adopt cloud-native machine learning solutions, reflecting a growing interest in modernising machine learning practices.
Katib is a Kubernetes-native hyperparameter tuning framework within Kubeflow that automates the process of finding the optimal hyperparameters for machine learning models to enhance performance.
Securing a Kubeflow cluster involves implementing Kubernetes RBAC for access control, using network policies to restrict traffic, and ensuring data encryption both at rest and in transit.
Troubleshooting in Kubeflow can involve examining logs, monitoring resource utilisation, using the Kubeflow dashboard for insights, and checking the status of pipeline runs to identify issues.
Delegates will gain access to various tools such as Jupyter Notebooks, Katib for hyperparameter tuning, Pipelines for workflow management, and various Kubernetes tools for orchestration.
Operators in Kubeflow include training operators for various frameworks like TensorFlow, PyTorch, and MXNet, enabling seamless integration and management of machine learning workloads.
Multi-user isolation in Kubeflow is achieved through namespace segmentation in Kubernetes, allowing different users to work in isolated environments while sharing the underlying infrastructure.
Fundamentals of Kubeflow include understanding Kubernetes concepts, building and deploying machine learning pipelines, managing datasets, and leveraging tools for training and tuning models.
Organisations benefit from Kubeflow by improving collaboration between data science and operations teams, enhancing the scalability of machine learning models, and accelerating time-to-market for AI solutions.
TensorFlow is a machine learning framework, while Kubeflow is a platform for deploying and managing machine learning workflows on Kubernetes, allowing the use of TensorFlow along with other tools.
To run a Kubeflow pipeline, users create a pipeline definition using Python, deploy it to the Kubeflow dashboard, and execute it while monitoring the progress and results through the interface.
Completing Kubeflow training opens up job opportunities such as machine learning engineer, data engineer, DevOps specialist, and cloud engineer, all focused on cloud-native solutions.
The average salary for professionals skilled in Kubeflow can range widely, typically from £50,000 to £90,000 annually, depending on experience and specific job roles.
Kubeflow certification is becoming increasingly popular in Fiji as organisations seek qualified professionals to support their machine learning initiatives and cloud adoption strategies.
To register for the course, visit The Knowledge Academy's website, navigate to the course page, and click on the registration button. Fill in the required details, select your preferred schedule, and complete the payment process.
The training fees for Kubeflow Trainingin Fiji starts from $2295
The Knowledge Academy is the Leading global training provider for Kubeflow Training.
Please see our DevOps Certification courses available in Fiji
Why choose us
Best price in the industry
You won't find better value in the marketplace. If you do find a lower price, we will beat it.
Many delivery methods
Flexible delivery methods are available depending on your learning style.
High quality resources
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
Kubeflow Training in Fiji
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