Kubeflow Training | DevOps in Seattle

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Kubeflow Training

Seattle

Gain a deep understanding of Kubeflow to customise resulting configuration files.
Get familiar with the Kubeflow pipelines for building and deploying ML workflow.
Acquire lucrative skills to run Kubeflow on Amazon Web Services and Google Cloud.

Dates & Prices Dates & Prices

Course information

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 Seattle 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 Seattle 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 Seattle 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 Seattle, 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 Seattle, 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

Our Seattle venue

Includes..

Free Wi-Fi

To make sure you’re always connected we offer completely free and easy to access wi-fi.

Air conditioned

To keep you comfortable during your course we offer a fully air conditioned environment.

Full IT support

IT support is on hand to sort out any unforseen issues that may arise.

Video equipment

This location has full video conferencing equipment.

Seattle is a West Coast city in America. Seattle has around 667,000 residents and is the largest city in the state of Washington and in the Pacific Northwest region of North America. The metropolitan area of Seattle has around 3.6 million residents. Seattle had a notable music scene between the 20’s and 50’s producing early careers for Ray Charles and Quincy Jones. Seattle is home to the University of Washington which was established in 1861. The University of Washington is a public research university and features one of the most notable medical schools in the world. The University of Washington has around 45,000 students and offers 140 departments with studies in courses such as; Arts and Science, Dentistry, Education, Engineering, Law, Medicine, Nursing, Pharmacy and Public Health. The University has a large sports department, and offers scholarships and opportunities in sports such as; Football, Soccer, Basketball, Softball and Rowing which is a long standing traditional at the University. The University of Washington Educational Outreach is another institute located in Seattle. The University was established in 1912. The University offers various different programs, classes and workshops in studies such as communication, English, Humanities, Social Sciences, Health Informatics and Health Information Management. Seattle is also the home to a number of smaller universities including Seattle University is a catholic university in Seattle that was established in 1891. Seattle University has over 7,500 students and offers courses in Business & Economics, Arts & Sciences, Humanities, Teaching, Nursing, Engineering, Theology and Law. Seattle Pacific University is another University situated in Seattle. This University was established in 1913 and is a Christian University. The Seattle Pacific University has around 4,000 students in attendance. The University offers courses in Fine Arts, Humanities, Science, Engineering, Social & Behavioural Sciences, Business, Education, Health, Psychology and Theology. Some notable alumni attended Seattle Pacific University like David T. Wong who was the co-inventor of Prozac and Dan Price who is the CEO of Gravity Payments.

Address

Silver Cloud Inn Lake Union
1150 Fairview Ave North
Seattle
Washington
98109

T: +1 7204454674

Ways to take this course

Experience live, interactive learning from home with The Knowledge Academy's Online Instructor-led Kubeflow Training | DevOps in Seattle. 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.

Learn more in our FAQs

Unlock your potential with The Knowledge Academy's Kubeflow Training | DevOps in Seattle, 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.

Learn more in our FAQs

Streamline large-scale training requirements with The Knowledge Academy's In-house/Onsite 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 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

Learn more in our FAQs

What our customers are saying

Kubeflow Training | DevOps in Seattle FAQs

What is Kubeflow?

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.

What are the prerequisites for this course?

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.

Who should attend this course?

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.

What is the goal of Kubeflow?

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.

What are the benefits of Kubeflow?

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.

What is the purpose of the Kubeflow pipeline?

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.

What will I learn in this Kubeflow Training Course?

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.

What companies actively recruit professionals certified in Kubeflow Training?

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.

How popular is the Kubeflow Training in Seattle?

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.

What is KATIB?

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.

How to secure a Cluster using Kubeflow?

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.

What are some ways to troubleshoot using Kubeflow?

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.

What tools will I get access to in Kubeflow Training?

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.

What are some operators in Kubeflow?

Operators in Kubeflow include training operators for various frameworks like TensorFlow, PyTorch, and MXNet, enabling seamless integration and management of machine learning workloads.

How does multi-user isolation work?

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.

What are some fundamentals of Kubeflow?

Fundamentals of Kubeflow include understanding Kubernetes concepts, building and deploying machine learning pipelines, managing datasets, and leveraging tools for training and tuning models.

How do organisations benefit from Kubeflow?

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.

Difference between TensorFlow and Kubeflow?

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.

How to run a Kubeflow pipeline?

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.

What job opportunities will I get on completing the Kubeflow training?

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.

What is the average salary that one can expect on completing the Kubeflow course?

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.

How popular is the Kubeflow certification in Seattle?

Kubeflow certification is becoming increasingly popular in Seattle as organisations seek qualified professionals to support their machine learning initiatives and cloud adoption strategies.

How can I register for the course?

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.

What is the cost/training fees for Kubeflow Training in Seattle?

The training fees for Kubeflow Trainingin Seattle starts from $2295

Which is the best training institute/provider of Kubeflow Training in Seattle?

The Knowledge Academy is the Leading global training provider for Kubeflow Training.

What are the best DevOps Certification courses in Seattle?

Please see our DevOps Certification courses available in Seattle

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

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