Kubeflow for AI/ML: Getting Started
Master Kubeflow, the Kubernetes-native toolkit for machine learning, and learn to deploy scalable ML systems across any cloud environment.
Master Kubeflow, the Kubernetes-native toolkit for machine learning, and learn to deploy scalable ML systems across any cloud environment.
This comprehensive course introduces Kubeflow, an open-source machine learning toolkit built for Kubernetes. Learn how to bridge the gap between DevOps and ML operations while deploying sophisticated machine learning applications. The course covers Kubeflow's core components, deployment options, and integrations, teaching you to implement MLOps practices effectively. You'll master practical skills in launching Kubeflow notebooks, pipelines, and understanding hyperparameter tuning with Katib. Perfect for engineers and data scientists seeking to leverage Kubernetes for ML workflows.
Instructors:
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What you'll learn
Understand MLOps principles and their relationship with DevOps
Master common machine learning platform patterns and solutions
Grasp the complete model development lifecycle
Learn to select and deploy appropriate Kubeflow distributions
Develop skills in launching Kubeflow notebooks and pipelines
Understand hyperparameter tuning with Katib
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 10 modules in this course
This course provides a comprehensive introduction to Kubeflow, the open-source machine learning toolkit built for Kubernetes environments. Students learn the fundamentals of MLOps and how it relates to DevOps practices, understanding the model development lifecycle and common machine learning platform patterns. The curriculum covers essential topics including Kubeflow deployment options, component architecture, and standard integrations. Practical hands-on experience is gained through working with Kubeflow notebooks, pipelines, and hyperparameter tuning tools. The course emphasizes real-world applications and best practices for implementing machine learning systems in production environments.
The Model Application Relationship and the Power of Reproducibility
Module 1
The Model Development Lifecycle
Module 2
MLOPs and the Rise of the Machine Learning Toolkit
Module 3
The Origin of Kubeflow
Module 4
Kubeflow Distributions
Module 5
The Kubeflow Dashboard and Notebooks
Module 6
The Unified Training Operator and Machine Learning
Module 7
Kubeflow Pipelines
Module 8
Conquering Katib
Module 9
Common Kubeflow Integrations
Module 10
Fee Structure
Instructor
1 Course
Versatile Solutions Engineer with a Focus on Data and Kubernetes
Chase Christensen is a Staff Solutions Engineer at TileDB, bringing a wealth of experience in presales engineering and solutions architecture. His career journey began in QA testing, where he developed a strong foundation in automated testing and deployment. Chase's expertise expanded during his time at Insight, where he managed a multi-vendor, multi-platform hybrid research and innovation lab, gaining valuable exposure to Kubernetes. Demonstrating his commitment to professional growth, he achieved the CKA, CKAD, and CKS certifications. Chase's passion for machine learning on Kubernetes led him to join Arrikto's Enterprise Kubeflow team, where he honed his skills in helping organizations adopt and leverage Kubeflow. With three years of experience in Kubeflow implementation and team training, Chase continues to advocate for open-source solutions as a transparent and people-centric approach to driving value for data professionals. Beyond his professional pursuits, Chase enjoys exploring the Colorado wilderness through hiking, balancing his technical expertise with a love for nature.
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Frequently asked questions
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