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Abhishek Kumar
In this course, you will learn how to effectively build end-to-end scalable, production grade machine learning workflows using Kubeflow.
Read more
In this course, you will learn how to effectively build end-to-end scalable, production grade machine learning workflows using Kubeflow.
Read more
In this course, you will learn how to effectively build end-to-end scalable, production grade machine learning workflows using Kubeflow.
Building production grade, scalable machine learning workflows is a complex and time-consuming task. In this course, Building End-to-end Machine Learning Workflows with Kubeflow 1, you will learn to use Kubeflow and discover how it can enable data scientists and machine learning engineers to build end-to-end machine learning workflows and perform rapid experimentation. First, you will delve into performing large scale distributed training. Next, you will explore hyperparameter tuning, model versioning, serverless model serving, and canary rollouts. Finally, you will learn how to build reproducible pipelines using various Kubeflow components, such as notebook server, fairing, metadata, katib, and Kubeflow pipelines. When you are finished with the course, you will be able to build end-to-end workflows for your machine learning and deep learning projects.
Register for this course and see more details by visiting:
OpenCourser.com/course/0zjw4x/building
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What's inside
Syllabus
Course Overview
Introduction
Setting up Kubeflow Environment
Exploring Kubeflow Components
Read more
Syllabus
Course Overview
Introduction
Setting up Kubeflow Environment
Exploring Kubeflow Components
Read more
Building Machine Learning Model on Kubeflow
Serving Machine Learning Model on Kubeflow
Build Machine Learning Pipeline Using Kubeflow Pipeline
What's Next?
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches data scientists and machine learning engineers how to build and integrate end-to-end ML workflows with Kubeflow
Covers topics such as large scale distributed training, hyperparameter tuning, model versioning, and canary rollouts, which are core skills for ML development
Uses Kubeflow, an open-source platform that is widely used in the industry for ML workflow management
Might not be suitable for beginners in ML as it assumes some prior knowledge in the field
Save this course
Save Building End-to-end Machine Learning Workflows with Kubeflow 1 to your list so you can find it easily later:
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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 Building End-to-end Machine Learning Workflows with Kubeflow 1 with these
activities:
Review past coursework on distributed training concepts
Show steps
Review the concepts of distributed training to refresh your knowledge before starting the course.
Browse courses on
Distributed Training
Show steps
-
Review notes and assignments from previous courses on distributed training
-
Complete practice problems and exercises on distributed training algorithms
Explore Kubeflow documentation and tutorials
Show steps
Gain a deeper understanding of Kubeflow by exploring its documentation and following guided tutorials.
Browse courses on
Kubeflow
Show steps
-
Read the Kubeflow documentation to familiarize yourself with its components and architecture
-
Follow official Kubeflow tutorials to build and deploy ML workflows
-
Explore community-created tutorials and resources on Kubeflow
Join a study group or online forum for Kubeflow
Show steps
Connect with other learners and experts to share knowledge, ask questions, and collaborate on projects.
Show steps
-
Find and join a study group or online forum focused on Kubeflow
-
Participate in discussions, ask questions, and share your experiences
-
Collaborate on projects and learn from others
Five other activities
Expand to see all activities and additional details
Show all eight activities
Explore Kubeflow Components
Show steps
Deepens understanding of the individual components that make up Kubeflow and their roles in building machine learning workflows.
Show steps
-
Follow guided tutorials on the official Kubeflow website
-
Experiment with different components using provided examples
-
Explore the documentation and resources for each component
Practice Kubeflow Commands
Show steps
Reinforces the understanding of Kubeflow commands and their usage in building machine learning workflows.
Browse courses on
Kubeflow
Show steps
-
Review the Kubeflow documentation for commands
-
Set up a Kubeflow environment
-
Execute various Kubeflow commands to perform tasks such as creating pipelines, deploying models, and managing resources
Practice building ML models on Kubeflow
Show steps
Develop proficiency in building and training ML models using Kubeflow through hands-on practice.
Show steps
-
Create a simple ML model and deploy it on Kubeflow
-
Experiment with different model architectures and hyperparameters
-
Optimize your models for performance and efficiency
Build an End-to-End Machine Learning Workflow
Show steps
Provides hands-on experience in designing and implementing complete machine learning workflows using Kubeflow.
Browse courses on
Kubeflow Pipelines
Show steps
-
Define the workflow steps and components
-
Create the necessary Kubeflow resources, such as pipelines and deployments
-
Configure the workflow parameters and dependencies
-
Run the workflow and monitor its progress
Develop a Machine Learning Model for a Specific Domain
Show steps
Applies the concepts learned in the course to a practical setting, enhancing problem-solving skills and domain knowledge.
Browse courses on
Machine Learning Projects
Show steps
-
Identify a real-world problem or dataset
-
Build a machine learning model using Kubeflow
-
Train and evaluate the model
-
Deploy the model and monitor its performance
Review past coursework on distributed training concepts
Show steps
Review the concepts of distributed training to refresh your knowledge before starting the course.
Browse courses on
Distributed Training
Show steps
Show steps
Show steps
- Review notes and assignments from previous courses on distributed training
- Complete practice problems and exercises on distributed training algorithms
Explore Kubeflow documentation and tutorials
Show steps
Gain a deeper understanding of Kubeflow by exploring its documentation and following guided tutorials.
Browse courses on
Kubeflow
Show steps
Show steps
Show steps
- Read the Kubeflow documentation to familiarize yourself with its components and architecture
- Follow official Kubeflow tutorials to build and deploy ML workflows
- Explore community-created tutorials and resources on Kubeflow
Join a study group or online forum for Kubeflow
Show steps
Connect with other learners and experts to share knowledge, ask questions, and collaborate on projects.
Show steps
Show steps
Show steps
- Find and join a study group or online forum focused on Kubeflow
- Participate in discussions, ask questions, and share your experiences
- Collaborate on projects and learn from others
Five other activities
Expand to see all activities and additional details
Show all eight activities
Explore Kubeflow Components
Show steps
Deepens understanding of the individual components that make up Kubeflow and their roles in building machine learning workflows.
Show steps
Show steps
Show steps
- Follow guided tutorials on the official Kubeflow website
- Experiment with different components using provided examples
- Explore the documentation and resources for each component
Practice Kubeflow Commands
Show steps
Reinforces the understanding of Kubeflow commands and their usage in building machine learning workflows.
Browse courses on
Kubeflow
Show steps
Show steps
Show steps
- Review the Kubeflow documentation for commands
- Set up a Kubeflow environment
- Execute various Kubeflow commands to perform tasks such as creating pipelines, deploying models, and managing resources
Practice building ML models on Kubeflow
Show steps
Develop proficiency in building and training ML models using Kubeflow through hands-on practice.
Show steps
Show steps
Show steps
- Create a simple ML model and deploy it on Kubeflow
- Experiment with different model architectures and hyperparameters
- Optimize your models for performance and efficiency
Build an End-to-End Machine Learning Workflow
Show steps
Provides hands-on experience in designing and implementing complete machine learning workflows using Kubeflow.
Browse courses on
Kubeflow Pipelines
Show steps
Show steps
Show steps
- Define the workflow steps and components
- Create the necessary Kubeflow resources, such as pipelines and deployments
- Configure the workflow parameters and dependencies
- Run the workflow and monitor its progress
Develop a Machine Learning Model for a Specific Domain
Show steps
Applies the concepts learned in the course to a practical setting, enhancing problem-solving skills and domain knowledge.
Browse courses on
Machine Learning Projects
Show steps
Show steps
Show steps
- Identify a real-world problem or dataset
- Build a machine learning model using Kubeflow
- Train and evaluate the model
- Deploy the model and monitor its performance
Career center
Learners who complete Building End-to-end Machine Learning Workflows with Kubeflow 1 will develop knowledge and skills
that may be useful to these careers:
Machine Learning Scientist
Machine Learning Scientist
Machine Learning Scientists research and develop new machine learning algorithms and techniques. Machine Learning Scientists typically hold a master's or PhD in computer science or a related field. Building End-to-end Machine Learning Workflows with Kubeflow 1 may be useful to this career path because it provides insights into the process of building and deploying machine learning models.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build, deploy, and maintain machine learning systems. Machine Learning Engineers typically hold a master's or PhD in computer science, statistics, or a related field. Building End-to-end Machine Learning Workflows with Kubeflow 1 may be useful to this career path because it provides hands-on experience deploying machine learning models in a production-ready environment.
Data Engineer
Data Engineer
Data Engineers design and build data pipelines that collect, process, and store data. Data Engineers typically hold a bachelor's degree in computer science or a related field. Building End-to-end Machine Learning Workflows with Kubeflow 1 may be useful to this career path because it teaches the skills needed to build and manage machine learning pipelines.
Data Science Manager
Data Science Manager
Data Science Managers lead teams of data scientists and machine learning engineers. Data Science Managers typically hold a master's or PhD in computer science or a related field. Building End-to-end Machine Learning Workflows with Kubeflow 1 may be useful to this career path because it provides insights into the process of managing machine learning projects.
Cloud Architect
Cloud Architect
Cloud Architects design and implement cloud computing solutions. Cloud Architects typically hold a bachelor's degree in computer science or a related field. Building End-to-end Machine Learning Workflows with Kubeflow 1 may be useful to this career path because it provides hands-on experience deploying machine learning models in a cloud environment.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. Quantitative Analysts typically hold a bachelor's degree in mathematics, statistics, or a related field. Building End-to-end Machine Learning Workflows with Kubeflow 1 may be useful to this career path because it provides insights into the process of using machine learning to solve financial problems.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve problems in a variety of industries, including manufacturing, logistics, and healthcare. Operations Research Analysts typically hold a bachelor's degree in mathematics, statistics, or a related field. Building End-to-end Machine Learning Workflows with Kubeflow 1 may be useful to this career path because it provides insights into the process of using machine learning to solve operational problems.
Data Analyst
Data Analyst
Data Analysts collect, process, and analyze data to identify trends and patterns. Data Analysts typically hold a bachelor's degree in statistics, mathematics, or a related field. Building End-to-end Machine Learning Workflows with Kubeflow 1 may be useful to this career path because it teaches the skills needed to build machine learning pipelines.
Software Development Manager
Software Development Manager
Software Development Managers lead teams of software engineers. Software Development Managers typically hold a bachelor's degree in computer science or a related field. Building End-to-end Machine Learning Workflows with Kubeflow 1 may be useful to this career path because it provides insights into the process of managing machine learning projects.
Research Scientist
Research Scientist
Research Scientists conduct research in a variety of fields, including computer science, biology, and physics. Research Scientists typically hold a master's or PhD in their field of study. Building End-to-end Machine Learning Workflows with Kubeflow 1 may be useful to this career path because it provides insights into the process of building and deploying machine learning models.
DevOps Engineer
DevOps Engineer
DevOps Engineers are responsible for bridging the gap between development and operations teams. DevOps Engineers typically hold a bachelor's degree in computer science or a related field. Building End-to-end Machine Learning Workflows with Kubeflow 1 may be useful to this career path because it teaches the skills needed to deploy and manage machine learning models in a production environment.
Data Scientist
Data Scientist
Data Scientists use scientific methods, processes, algorithms, and systems to extract knowledge and insights from data in various forms, both structured and unstructured. Building End-to-end Machine Learning Workflows with Kubeflow 1 may be useful to this career path because it teaches the skills needed to build scalable, production grade machine learning workflows.
Software Engineer
Software Engineer
Software Engineers design, develop, test, and maintain software systems. Software Engineers typically hold a bachelor's degree in computer science or a related field. Building End-to-end Machine Learning Workflows with Kubeflow 1 may be useful to this career path because it helps build a foundation in deploying machine learning models using cutting-edge technologies such as Kubernetes.
Product Manager
Product Manager
Product Managers are responsible for defining and managing the roadmap for a product. Product Managers typically hold a bachelor's degree in business or a related field. Building End-to-end Machine Learning Workflows with Kubeflow 1 may be useful to this career path because it provides insights into the process of building and deploying machine learning models.
Business Analyst
Business Analyst
Business Analysts analyze business processes and develop solutions to improve efficiency and effectiveness. Business Analysts typically hold a bachelor's degree in business or a related field. Building End-to-end Machine Learning Workflows with Kubeflow 1 may be useful to this career path because it provides insights into the process of using machine learning to solve business problems.
For more career information including salaries, visit:
OpenCourser.com/course/0zjw4x/building
Reading list
We've selected ten books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Building End-to-end Machine Learning Workflows with Kubeflow 1.
Is about the implementation of Kubernetes.
Is about using TensorFlow 2 and Keras to build and train deep learning models.
Is about the design patterns for machine learning.
Is about using Python for data analysis.
Is about the implementation of machine learning.
Is about the fundamentals of data science.
Is about the fundamentals of deep learning.
Is about the implementation of TensorFlow.
Is about the algorithms for machine learning.
Save
Is about the fundamentals of deep learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/0zjw4x/building
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Time
210 hours
Level
Introductory
Via
Pluralsight
Instructor
Abhishek Kumar
Language
English
Good to know
Teaches data scientists and machine learning engineers how to build and integrate end-to-end ML workflows with Kubeflow
Covers topics such as large scale distributed training, hyperparameter tuning, model versioning, and canary rollouts, which are core skills for ML development
Uses Kubeflow, an open-source platform that is widely used in the industry for ML workflow management
Might not be suitable for beginners in ML as it assumes some prior knowledge in the field
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