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Autoscaling TensorFlow Model Deployments with TF Serving and Kubernetes
Google Cloud Training
Google Cloud Training
This is a self-paced lab that takes place in the Google Cloud console.
Read more
This is a self-paced lab that takes place in the Google Cloud console.
Read more
This is a self-paced lab that takes place in the Google Cloud console.
AutoML Vision helps developers with limited ML expertise train high quality image recognition models. In this hands-on lab, you will learn how to train a custom model to recognize different types of clouds (cumulus, cumulonimbus, etc.).
Register for this course and see more details by visiting:
OpenCourser.com/course/q4xxnt/autoscaling
What's inside
Syllabus
Autoscaling TensorFlow Model Deployments with TF Serving and Kubernetes
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Taught by Google, a prominent player in the cloud computing industry
Develops image recognition models, a valuable skill in the AI and computer vision fields
Well-suited for aspiring data scientists and engineers
Practical hands-on lab experience for building real-world models
Leverages Google Cloud's Kubernetes Autoscaling technology
Requires prior knowledge of TensorFlow Serving and Kubernetes, which may limit accessibility for beginners
Save this course
Save Autoscaling TensorFlow Model Deployments with TF Serving and Kubernetes 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 Autoscaling TensorFlow Model Deployments with TF Serving and Kubernetes with these
activities:
Review Cloud Computing Basics
Show steps
Brush up on the core concepts of cloud computing to strengthen your understanding of the course material.
Browse courses on
Cloud Computing
Show steps
-
Reread introductory articles or textbooks on cloud computing principles.
-
Review online tutorials or documentation on cloud service models, deployment strategies, and security considerations.
-
Complete practice questions or exercises to test your understanding of cloud computing fundamentals.
Practice Cloud Deployment with Kubernetes
Show steps
Reinforce your understanding of cloud deployment and container orchestration for better implementation of AutoML Vision models.
Browse courses on
Kubernetes
Show steps
-
Set up a Kubernetes cluster on your local machine or cloud platform.
-
Deploy a simple application using Kubernetes.
-
Monitor and manage your Kubernetes deployment.
Explore AutoML Vision Tutorials
Show steps
Follow guided tutorials to gain practical experience with AutoML Vision and enhance your understanding of its capabilities.
Browse courses on
AutoML Vision
Show steps
-
Visit the official AutoML Vision documentation and tutorials.
-
Work through step-by-step guides on how to train and deploy custom image recognition models.
-
Experiment with different parameters and evaluate model performance.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Join AutoML Vision Study Group
Show steps
Engage with peers in a collaborative environment to exchange knowledge, discuss challenges, and reinforce your learning.
Browse courses on
AutoML Vision
Show steps
-
Find or create an online study group focused on AutoML Vision.
-
Participate in regular discussions and share your understanding of the course material.
-
Collaborate on projects or assignments to deepen your comprehension.
Attend Machine Learning Conference
Show steps
Gain insights from industry experts and learn about the latest advancements in machine learning and cloud technologies.
Browse courses on
Machine Learning
Show steps
-
Identify and register for relevant conferences or workshops focused on machine learning and cloud computing.
-
Attend sessions and presentations on topics related to AutoML Vision and cloud deployment.
-
Network with professionals and expand your knowledge base.
Create AutoML Vision Blog Post
Show steps
Share your knowledge and insights on AutoML Vision by creating a blog post that covers its capabilities, applications, and best practices.
Browse courses on
AutoML Vision
Show steps
-
Choose a specific aspect of AutoML Vision that interests you.
-
Research and gather information from reliable sources.
-
Write a well-structured blog post that is informative and engaging.
-
Publish your blog post on a relevant platform.
Showcase Cloud and AutoML Vision Expertise
Show steps
Apply your skills to create a tangible deliverable that demonstrates your proficiency in cloud computing and AutoML Vision.
Browse courses on
Cloud Computing
Show steps
-
Identify a real-world problem or opportunity that can be solved using AutoML Vision.
-
Develop a plan for a cloud-based solution using AutoML Vision.
-
Create a prototype or MVP of your solution.
-
Showcase your project to potential stakeholders or industry professionals.
Contribute to Open-Source AutoML Projects
Show steps
Make a meaningful contribution to the AutoML community and enhance your understanding of the technology.
Browse courses on
AutoML
Show steps
-
Identify open-source repositories related to AutoML or cloud image recognition.
-
Review the project documentation and identify areas where you can contribute.
-
Submit bug reports, feature requests, or code contributions.
Review Cloud Computing Basics
Show steps
Brush up on the core concepts of cloud computing to strengthen your understanding of the course material.
Browse courses on
Cloud Computing
Show steps
Show steps
Show steps
- Reread introductory articles or textbooks on cloud computing principles.
- Review online tutorials or documentation on cloud service models, deployment strategies, and security considerations.
- Complete practice questions or exercises to test your understanding of cloud computing fundamentals.
Practice Cloud Deployment with Kubernetes
Show steps
Reinforce your understanding of cloud deployment and container orchestration for better implementation of AutoML Vision models.
Browse courses on
Kubernetes
Show steps
Show steps
Show steps
- Set up a Kubernetes cluster on your local machine or cloud platform.
- Deploy a simple application using Kubernetes.
- Monitor and manage your Kubernetes deployment.
Explore AutoML Vision Tutorials
Show steps
Follow guided tutorials to gain practical experience with AutoML Vision and enhance your understanding of its capabilities.
Browse courses on
AutoML Vision
Show steps
Show steps
Show steps
- Visit the official AutoML Vision documentation and tutorials.
- Work through step-by-step guides on how to train and deploy custom image recognition models.
- Experiment with different parameters and evaluate model performance.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Join AutoML Vision Study Group
Show steps
Engage with peers in a collaborative environment to exchange knowledge, discuss challenges, and reinforce your learning.
Browse courses on
AutoML Vision
Show steps
Show steps
Show steps
- Find or create an online study group focused on AutoML Vision.
- Participate in regular discussions and share your understanding of the course material.
- Collaborate on projects or assignments to deepen your comprehension.
Attend Machine Learning Conference
Show steps
Gain insights from industry experts and learn about the latest advancements in machine learning and cloud technologies.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify and register for relevant conferences or workshops focused on machine learning and cloud computing.
- Attend sessions and presentations on topics related to AutoML Vision and cloud deployment.
- Network with professionals and expand your knowledge base.
Create AutoML Vision Blog Post
Show steps
Share your knowledge and insights on AutoML Vision by creating a blog post that covers its capabilities, applications, and best practices.
Browse courses on
AutoML Vision
Show steps
Show steps
Show steps
- Choose a specific aspect of AutoML Vision that interests you.
- Research and gather information from reliable sources.
- Write a well-structured blog post that is informative and engaging.
- Publish your blog post on a relevant platform.
Showcase Cloud and AutoML Vision Expertise
Show steps
Apply your skills to create a tangible deliverable that demonstrates your proficiency in cloud computing and AutoML Vision.
Browse courses on
Cloud Computing
Show steps
Show steps
Show steps
- Identify a real-world problem or opportunity that can be solved using AutoML Vision.
- Develop a plan for a cloud-based solution using AutoML Vision.
- Create a prototype or MVP of your solution.
- Showcase your project to potential stakeholders or industry professionals.
Contribute to Open-Source AutoML Projects
Show steps
Make a meaningful contribution to the AutoML community and enhance your understanding of the technology.
Browse courses on
AutoML
Show steps
Show steps
Show steps
- Identify open-source repositories related to AutoML or cloud image recognition.
- Review the project documentation and identify areas where you can contribute.
- Submit bug reports, feature requests, or code contributions.
Career center
Learners who complete Autoscaling TensorFlow Model Deployments with TF Serving and Kubernetes will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists develop, build, and manage machine learning and deep learning models. They also interpret results and provide insights to stakeholders. This course can help Data Scientists learn how to deploy and autoscale TensorFlow models using TF Serving and Kubernetes, which are essential technologies for productionizing machine learning models. By taking this course, Data Scientists can gain hands-on experience in these technologies and enhance their skills in deploying and managing machine learning models in a production environment.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, implement, and maintain machine learning systems. They also collaborate with Data Scientists and other stakeholders to develop and deploy machine learning solutions. This course can help Machine Learning Engineers learn how to deploy and autoscale TensorFlow models using TF Serving and Kubernetes, which are essential technologies for productionizing machine learning models. By taking this course, Machine Learning Engineers can gain hands-on experience in these technologies and enhance their skills in deploying and managing machine learning models in a production environment.
Cloud Engineer
Cloud Engineer
Cloud Engineers design, build, and manage cloud computing systems. They also collaborate with other IT professionals to develop and deploy cloud-based solutions. This course can help Cloud Engineers learn how to deploy and autoscale TensorFlow models using TF Serving and Kubernetes, which are essential technologies for productionizing machine learning models in the cloud. By taking this course, Cloud Engineers can gain hands-on experience in these technologies and enhance their skills in deploying and managing machine learning models in a cloud environment.
DevOps Engineer
DevOps Engineer
DevOps Engineers work with software developers and IT operations teams to ensure that software is deployed and managed efficiently and effectively. This course can help DevOps Engineers learn how to deploy and autoscale TensorFlow models using TF Serving and Kubernetes, which are essential technologies for deploying and managing machine learning models in a production environment. By taking this course, DevOps Engineers can gain hands-on experience in these technologies and enhance their skills in deploying and managing machine learning models in a production environment.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They also collaborate with other IT professionals to develop and deploy software solutions. This course can help Software Engineers learn how to deploy and autoscale TensorFlow models using TF Serving and Kubernetes, which are essential technologies for deploying and managing machine learning models in a production environment. By taking this course, Software Engineers can gain hands-on experience in these technologies and enhance their skills in deploying and managing machine learning models in a production environment.
Data Analyst
Data Analyst
Data Analysts analyze data to identify trends and patterns. They also use data to develop insights and make recommendations. This course can help Data Analysts learn how to deploy and autoscale TensorFlow models using TF Serving and Kubernetes, which are essential technologies for deploying and managing machine learning models in a production environment. By taking this course, Data Analysts can gain hands-on experience in these technologies and enhance their skills in deploying and managing machine learning models in a production environment.
Business Analyst
Business Analyst
Business Analysts work with businesses to identify and analyze business needs. They also develop and implement solutions to improve business processes. This course can help Business Analysts learn how to deploy and autoscale TensorFlow models using TF Serving and Kubernetes, which are essential technologies for deploying and managing machine learning models in a production environment. By taking this course, Business Analysts can gain hands-on experience in these technologies and enhance their skills in deploying and managing machine learning models in a production environment.
Product Manager
Product Manager
Product Managers are responsible for the development and management of products. They also work with engineers and other stakeholders to ensure that products meet the needs of customers. This course can help Product Managers learn how to deploy and autoscale TensorFlow models using TF Serving and Kubernetes, which are essential technologies for deploying and managing machine learning models in a production environment. By taking this course, Product Managers can gain hands-on experience in these technologies and enhance their skills in deploying and managing machine learning models in a production environment.
Project Manager
Project Manager
Project Managers are responsible for the planning and execution of projects. They also work with stakeholders to ensure that projects are completed on time and within budget. This course can help Project Managers learn how to deploy and autoscale TensorFlow models using TF Serving and Kubernetes, which are essential technologies for deploying and managing machine learning models in a production environment. By taking this course, Project Managers can gain hands-on experience in these technologies and enhance their skills in deploying and managing machine learning models in a production environment.
Technical Writer
Technical Writer
Technical Writers create documentation for software and other technical products. They also work with engineers and other stakeholders to ensure that documentation is accurate and easy to understand. This course can help Technical Writers learn how to deploy and autoscale TensorFlow models using TF Serving and Kubernetes, which are essential technologies for deploying and managing machine learning models in a production environment. By taking this course, Technical Writers can gain hands-on experience in these technologies and enhance their skills in deploying and managing machine learning models in a production environment.
Sales Engineer
Sales Engineer
Sales Engineers work with customers to identify and sell products and services. They also provide technical support to customers. This course can help Sales Engineers learn how to deploy and autoscale TensorFlow models using TF Serving and Kubernetes, which are essential technologies for deploying and managing machine learning models in a production environment. By taking this course, Sales Engineers can gain hands-on experience in these technologies and enhance their skills in deploying and managing machine learning models in a production environment.
Customer Success Manager
Customer Success Manager
Customer Success Managers work with customers to ensure that they are successful with their products and services. They also provide support and training to customers. This course can help Customer Success Managers learn how to deploy and autoscale TensorFlow models using TF Serving and Kubernetes, which are essential technologies for deploying and managing machine learning models in a production environment. By taking this course, Customer Success Managers can gain hands-on experience in these technologies and enhance their skills in deploying and managing machine learning models in a production environment.
Marketing Manager
Marketing Manager
Marketing Managers are responsible for the development and execution of marketing campaigns. They also work with other stakeholders to ensure that marketing campaigns are effective. This course can help Marketing Managers learn how to deploy and autoscale TensorFlow models using TF Serving and Kubernetes, which are essential technologies for deploying and managing machine learning models in a production environment. By taking this course, Marketing Managers can gain hands-on experience in these technologies and enhance their skills in deploying and managing machine learning models in a production environment.
Financial Analyst
Financial Analyst
Financial Analysts analyze financial data to identify trends and patterns. They also use data to develop insights and make recommendations. This course may help Financial Analysts learn how to deploy and autoscale TensorFlow models using TF Serving and Kubernetes, which are essential technologies for deploying and managing machine learning models in a production environment. By taking this course, Financial Analysts can gain hands-on experience in these technologies and enhance their skills in deploying and managing machine learning models in a production environment.
Human Resources Manager
Human Resources Manager
Human Resources Managers are responsible for the management of human resources within an organization. They also work with other stakeholders to ensure that human resources policies and procedures are effective. This course may help Human Resources Managers learn how to deploy and autoscale TensorFlow models using TF Serving and Kubernetes, which are essential technologies for deploying and managing machine learning models in a production environment. By taking this course, Human Resources Managers can gain hands-on experience in these technologies and enhance their skills in deploying and managing machine learning models in a production environment.
For more career information including salaries, visit:
OpenCourser.com/course/q4xxnt/autoscaling
Reading list
We've selected seven 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
Autoscaling TensorFlow Model Deployments with TF Serving and Kubernetes.
Provides a comprehensive overview of speech and language processing, with a focus on practical applications.
Comprehensive guide to Kubernetes, providing a deep understanding of its architecture, features, and best practices for deploying and managing containerized applications.
Comprehensive guide to using transformers, a popular type of deep learning model for natural language processing.
Comprehensive guide to using Keras, a popular high-level neural networks API for TensorFlow.
Provides a comprehensive guide to deploying and managing machine learning models on Kubernetes. It covers topics such as model serving, autoscaling, and monitoring.
Provides a practical guide to deploying and managing machine learning models on Kubernetes. It covers topics such as model packaging, deployment strategies, and monitoring.
Provides a comprehensive guide to deploying machine learning models with TensorFlow Serving. It covers topics such as model export, serving configurations, and performance optimization.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/q4xxnt/autoscaling
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Effort
1 hour 30 minutes
Level
Intermediate
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
English
Good to know
Taught by Google, a prominent player in the cloud computing industry
Develops image recognition models, a valuable skill in the AI and computer vision fields
Well-suited for aspiring data scientists and engineers
Practical hands-on lab experience for building real-world models
Leverages Google Cloud's Kubernetes Autoscaling technology
Requires prior knowledge of TensorFlow Serving and Kubernetes, which may limit accessibility for beginners
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