Introduction to AI/ML Toolkits with Kubeflow from edX

Machine learning and AI are rapidly transforming the world, impacting organizations of all sizes. As executives push for AI/ML strategies, DevOps teams have been upskilling and bridging the gap between operations and development for the last several years for traditional applications. The complex machine learning application arrives just as cross-team collaboration becomes familiar.

These data-dependent applications present fresh challenges for deployment and development, demanding expertise from developers and data scientists, data engineers, and machine learning engineers. How can existing engineers, with their container, Kubernetes, and cloud knowledge, navigate this terrain? Can non-engineers seeking smoother data-intensive projects find common ground with statistically-savvy data scientists? We think so! Enter Kubeflow, an open source, Kubernetes-powered toolkit that enables teams of any scale or maturity to harness the potential of machine learning. Rather than reinventing the wheel, Kubeflow simplifies the deployment of proven open-source ML systems across any cloud and even on-premise

This course begins with Kubeflow, covering its origins, deployment options, individual components, and standard integrations. By the end, you'll grasp how MLOPs can ensure the successful production of ML systems, how Kubeflow opens up ML for everyone, regardless of scale, understand how to choose the ideal Kubeflow distribution for your needs so you can see Kubeflow’s "simple, portable, scalable" promise in action, and launch your own Kubeflow project. We will even touch upon some additional open source integrations so you can make Kubeflow work for you!

This course caters to everyone wanting to leverage the power of machine learning. Whether you're an engineer, data scientist, or simply curious about Kubeflow, join us and discover how you can contribute to the future of machine learning!

What you'll learn

Discuss the value of MLOPs for production systems and how it relates to DevOps
Recognize common machine learning platform patterns and the problems they seek to solve
Explain the model development lifecycle
Define and identify common machine learning frameworks
Discuss the value proposition and goal of the universal training operator
Research and select a Kubeflow distribution based on your needs or, at the very least, have an informed conversation with a vendor.
Launch and leverage a Kubeflow Notebook.
Launch a primary Kubeflow pipeline.
Discuss additional popular Kubeflow integrations.
Familiarize yourself with Katib and Hyperparameter tuning

What's inside

Syllabus

Course Introduction: Welcome!

Chapter 1: The Model Application Relationship and the Power of Reproducibility

Chapter 2: The Model Development Lifecycle

Chapter 3: MLOPs and the Rise of the Machine Learning Toolkit

Chapter 4: The Origin of Kubeflow

Chapter 5: Kubeflow Distributions

Chapter 6: The Kubeflow Dashboard and Notebooks

Chapter 7: The Unified Training Operator and Machine Learning

Chapter 8: Kubeflow Pipelines

Chapter 9: Conquering Katib

Chapter 10: Common Kubeflow Integrations

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Taught by Chase Christensen, who are recognized for their work in DevOps and MLOPs

Provides a comprehensive study of Kubeflow, an open source toolkit for deploying machine learning applications

Explores the model development lifecycle, a core concept in machine learning

Teaches how to choose the ideal Kubeflow distribution for your needs, ensuring that you can leverage its benefits effectively

Develops skills in launching and leveraging Kubeflow Notebooks and Pipelines, providing hands-on experience with essential Kubeflow components

Covers additional popular Kubeflow integrations, expanding your knowledge and capabilities with the toolkit

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Introduction to AI/ML Toolkits with Kubeflow with these activities:

Organize a Study Group for Kubeflow

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Enhance your learning by connecting with fellow students and engaging in collaborative discussions.

Browse courses on Kubeflow

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Find interested participants
Establish meeting times and agenda
Facilitate discussions and knowledge sharing

Write a Blog Post on Kubeflow for Beginners

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Demonstrate your grasp of Kubeflow fundamentals by creating a resource that helps others understand the basics.

Browse courses on Kubeflow

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Research and gather information
Craft an engaging and informative blog post
Share your blog post on relevant platforms

Hands-on Kubernetes and Cloud Deployment Exercises

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Master the practical aspects of cloud deployment by completing a series of hands-on exercises.

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Set up a cloud environment
Deploy Kubernetes clusters
Deploy and manage applications on Kubernetes

Seven other activities

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Attend a Kubeflow Workshop or Meetup

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Expand your knowledge and network with experts by attending a Kubeflow workshop or meetup.

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Research upcoming events
Register and attend
Engage with speakers and attendees

Follow Jupyter Notebook Tutorials on Kubeflow

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Enhance your understanding of Kubeflow by working through hands-on tutorials in Jupyter Notebooks.

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Explore Kubeflow's official tutorial materials
Find additional tutorials from community sources
Practice using Kubeflow components in a controlled environment

Reinforcing Kubeflow Concepts

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Reinforce your understanding of Kubeflow's fundamentals by engaging with tutorials that provide hands-on experience.

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Identify reliable tutorial resources online.
Follow step-by-step instructions to complete tutorials.
Experiment with different Kubeflow components.
Troubleshoot any issues encountered during the tutorials.

Develop a Machine Learning Pipeline with Kubeflow

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Solidify your understanding of MLOps principles by creating a complete end-to-end machine learning pipeline.

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Design a pipeline architecture
Use Kubeflow Pipelines to define the workflow
Deploy and monitor the pipeline

Kubeflow Implementation Exercises

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Solidify your grasp of Kubeflow's practical applications by working through exercises that simulate real-world scenarios.

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Download the necessary materials for the exercises.
Set up a Kubeflow environment.
Deploy and manage machine learning models.
Monitor and optimize your Kubeflow deployments.
Troubleshoot common Kubeflow issues.

Participate in a Kubeflow Hackathon

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Test your skills and solve real-world problems by participating in a Kubeflow hackathon.

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Present your findings and compete for prizes
Find and register for a relevant hackathon
Form a team or work individually
Design and implement a solution

Kubeflow Deployment Project

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Demonstrate your proficiency in Kubeflow by designing, implementing, and deploying a machine learning solution that addresses a specific business problem.

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Identify a business problem that can be solved with Kubeflow.
Design a Kubeflow architecture to address the problem.
Implement the Kubeflow solution using appropriate tools and techniques.
Deploy the Kubeflow solution in a production environment.
Monitor and evaluate the performance of the Kubeflow solution.

Career center

Learners who complete Introduction to AI/ML Toolkits with Kubeflow will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists use their knowledge of mathematics, statistics, and computer science to extract insights from data. This course can help you develop the skills you need for this role, including an understanding of machine learning models and how to use Kubeflow to deploy them.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers are typically responsible for designing, developing, and deploying machine learning models. This course can help you develop the skills you need for this role, including an understanding of the model development lifecycle, common machine learning frameworks, and how to use Kubeflow to deploy machine learning models.

See salaries and explore the career path for Machine Learning Engineer

Business Analyst

Business Analysts use their knowledge of business and technology to identify and solve business problems. This course can help you develop the skills you need for this role, including an understanding of how to use machine learning to solve business problems.

See salaries and explore the career path for Business Analyst

Operations Research Analyst

Operations Research Analysts use their knowledge of mathematics and computer science to solve business problems. This course can help you develop the skills you need for this role, including an understanding of how to use machine learning to solve business problems.

See salaries and explore the career path for Operations Research Analyst

Statistician

Statisticians use their knowledge of statistics to collect, analyze, and interpret data. This course can help you develop the skills you need for this role, including an understanding of how to use machine learning to analyze data.

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Software Engineer

Software Engineers design, develop, and maintain software systems. This course can help you develop the skills you need for this role, including an understanding of how to use Kubernetes and Kubeflow to deploy machine learning models.

See salaries and explore the career path for Software Engineer

Data Analyst

Data Analysts use their knowledge of statistics and data analysis techniques to extract insights from data. This course can help you develop the skills you need for this role, including an understanding of how to use machine learning to analyze data.

See salaries and explore the career path for Data Analyst

Risk Analyst

Risk Analysts use their knowledge of mathematics and statistics to assess and manage risk. This course can help you develop the skills you need for this role, including an understanding of how to use machine learning to assess and manage risk.

See salaries and explore the career path for Risk Analyst

Product Manager

Product Managers are responsible for the development and launch of new products. This course can help you develop the skills you need for this role, including an understanding of how to use machine learning to improve products.

See salaries and explore the career path for Product Manager

Solutions Architect

Solutions Architects design and implement software solutions for clients. This course can help you develop the skills you need for this role, including an understanding of how to use Kubernetes and Kubeflow to deploy machine learning models.

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Data Engineer

Data Engineers design and build data pipelines. This course can help you develop the skills you need for this role, including an understanding of how to use Kubeflow to deploy machine learning models on data pipelines.

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Cloud Architect

Cloud Architects design and implement cloud-based solutions. This course can help you develop the skills you need for this role, including an understanding of how to use Kubernetes and Kubeflow to deploy machine learning models in the cloud.

See salaries and explore the career path for Cloud Architect

Quantitative Analyst

Quantitative Analysts use their knowledge of mathematics and statistics to analyze financial data. This course can help you develop the skills you need for this role, including an understanding of how to use machine learning to analyze financial data.

See salaries and explore the career path for Quantitative Analyst

Actuary

Actuaries use their knowledge of mathematics and statistics to assess and manage risk in the insurance industry. This course can help you develop the skills you need for this role, including an understanding of how to use machine learning to assess and manage risk in the insurance industry.

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DevOps Engineer

DevOps Engineers work to bridge the gap between development and operations teams. This course can help you develop the skills you need for this role, including an understanding of how to use Kubeflow to deploy machine learning models.

See salaries and explore the career path for DevOps Engineer