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Charles Ivan Niswander II
In this project-based course, you will learn step-by-step procedures for serving Tensorflow models with a RESTful API.
We will learn to save a Tensorflow object as a servable, deploy servables in Docker containers, as well as how to test our API endpoints...
Read more
In this project-based course, you will learn step-by-step procedures for serving Tensorflow models with a RESTful API.
We will learn to save a Tensorflow object as a servable, deploy servables in Docker containers, as well as how to test our API endpoints...
Read more
In this project-based course, you will learn step-by-step procedures for serving Tensorflow models with a RESTful API.
We will learn to save a Tensorflow object as a servable, deploy servables in Docker containers, as well as how to test our API endpoints and optimize our API response time.
I would encourage learners to experiment with the tools and methods discussed in this course. The learner is highly encouraged to experiment beyond the scope of the course.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/5irz72/serving
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Reviews summary
Tutorial recreation
This course has a single 2-star review indicating that it mostly only covers material that is already found in tutorials and reproduced from other content on the web.
Mostly a recreation of a known tutorial.
"Terrible course!!Why author copy paste tutorial ?"
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 Serving Tensorflow Models with a REST API with these
activities:
TensorFlow Review
Show steps
Review the basics of TensorFlow before starting the course to ensure a strong foundation.
Browse courses on
TensorFlow
Show steps
-
Review the TensorFlow documentation
-
Complete the TensorFlow tutorial
-
Review your notes from previous TensorFlow courses or projects
Review Tensorflow and REST APIs
Show steps
Reviewing the fundamentals of Tensorflow and REST APIs will provide stronger comprehension when the course begins.
Browse courses on
TensorFlow
Show steps
-
Review Tensorflow tutorials and documentation
-
Practice creating Tensorflow models
-
Explore REST API concepts and frameworks
Deep Learning with Python
Show steps
Read a book about deep learning with Python to expand your knowledge of TensorFlow's underlying principles.
Browse courses on
TensorFlow
Show steps
-
Find a book about deep learning with Python
-
Read the book and take notes
-
Complete the exercises in the book
Nine other activities
Expand to see all activities and additional details
Show all 12 activities
Follow Docker tutorials
Show steps
Completing Docker tutorials will provide practical experience in deploying servables.
Browse courses on
Docker
Show steps
-
Locate and review Docker documentation
-
Complete introductory Docker tutorials
-
Experiment with deploying servables in Docker containers
TensorFlow Tutorial
Show steps
Review the official TensorFlow tutorial to become familiar with the basics of the framework.
Browse courses on
TensorFlow
Show steps
-
Follow the TensorFlow website tutorial
-
Complete the exercises in the tutorial
-
Review the documentation for the TensorFlow API
TensorFlow Practice Exercises
Show steps
Solve coding exercises to reinforce your understanding of TensorFlow concepts.
Browse courses on
TensorFlow
Show steps
-
Find coding exercises online or in textbooks
-
Solve the exercises using TensorFlow
-
Review your solutions and identify areas for improvement
Write a blog post on Tensorflow model deployment
Show steps
Writing a blog post will help you solidify your understanding of the course concepts and share your knowledge with others.
Show steps
-
Choose a topic related to Tensorflow model deployment
-
Research and gather information
-
Write and edit your blog post
-
Publish and promote your blog post
Create a RESTful API for a Tensorflow model
Show steps
Developing a RESTful API for a Tensorflow model will reinforce the concepts taught in the course.
Show steps
-
Design and plan the API
-
Implement the API using a framework
-
Test and deploy the API
-
Optimize API response time
TensorFlow Blog Post
Show steps
Write a blog post about a TensorFlow project you worked on.
Browse courses on
TensorFlow
Show steps
-
Choose a TensorFlow project to write about
-
Research and gather information about the project
-
Write the blog post, explaining the project and sharing your insights
TensorFlow Project
Show steps
Build a TensorFlow project to apply your skills and deepen your understanding.
Browse courses on
TensorFlow
Show steps
-
Choose a project idea
-
Design and implement the project
-
Test and evaluate the project
TensorFlow Mentor
Show steps
Mentor other students who are learning TensorFlow to deepen your understanding and help others succeed.
Browse courses on
TensorFlow
Show steps
-
Join a TensorFlow online community or forum
-
Answer questions and provide guidance to other TensorFlow users
-
Organize or participate in TensorFlow study groups
Contribute to TensorFlow
Show steps
Contribute to the TensorFlow open-source project to gain experience and make a meaningful impact.
Browse courses on
TensorFlow
Show steps
-
Find an issue on the TensorFlow GitHub repository
-
Fix the issue and submit a pull request
-
Review the code and provide feedback to other contributors
TensorFlow Review
Show steps
Review the basics of TensorFlow before starting the course to ensure a strong foundation.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Review the TensorFlow documentation
- Complete the TensorFlow tutorial
- Review your notes from previous TensorFlow courses or projects
Review Tensorflow and REST APIs
Show steps
Reviewing the fundamentals of Tensorflow and REST APIs will provide stronger comprehension when the course begins.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Review Tensorflow tutorials and documentation
- Practice creating Tensorflow models
- Explore REST API concepts and frameworks
Deep Learning with Python
Show steps
Read a book about deep learning with Python to expand your knowledge of TensorFlow's underlying principles.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Find a book about deep learning with Python
- Read the book and take notes
- Complete the exercises in the book
Nine other activities
Expand to see all activities and additional details
Show all 12 activities
Follow Docker tutorials
Show steps
Completing Docker tutorials will provide practical experience in deploying servables.
Browse courses on
Docker
Show steps
Show steps
Show steps
- Locate and review Docker documentation
- Complete introductory Docker tutorials
- Experiment with deploying servables in Docker containers
TensorFlow Tutorial
Show steps
Review the official TensorFlow tutorial to become familiar with the basics of the framework.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Follow the TensorFlow website tutorial
- Complete the exercises in the tutorial
- Review the documentation for the TensorFlow API
TensorFlow Practice Exercises
Show steps
Solve coding exercises to reinforce your understanding of TensorFlow concepts.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Find coding exercises online or in textbooks
- Solve the exercises using TensorFlow
- Review your solutions and identify areas for improvement
Write a blog post on Tensorflow model deployment
Show steps
Writing a blog post will help you solidify your understanding of the course concepts and share your knowledge with others.
Show steps
Show steps
Show steps
- Choose a topic related to Tensorflow model deployment
- Research and gather information
- Write and edit your blog post
- Publish and promote your blog post
Create a RESTful API for a Tensorflow model
Show steps
Developing a RESTful API for a Tensorflow model will reinforce the concepts taught in the course.
Show steps
Show steps
Show steps
- Design and plan the API
- Implement the API using a framework
- Test and deploy the API
- Optimize API response time
TensorFlow Blog Post
Show steps
Write a blog post about a TensorFlow project you worked on.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Choose a TensorFlow project to write about
- Research and gather information about the project
- Write the blog post, explaining the project and sharing your insights
TensorFlow Project
Show steps
Build a TensorFlow project to apply your skills and deepen your understanding.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Choose a project idea
- Design and implement the project
- Test and evaluate the project
TensorFlow Mentor
Show steps
Mentor other students who are learning TensorFlow to deepen your understanding and help others succeed.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Join a TensorFlow online community or forum
- Answer questions and provide guidance to other TensorFlow users
- Organize or participate in TensorFlow study groups
Contribute to TensorFlow
Show steps
Contribute to the TensorFlow open-source project to gain experience and make a meaningful impact.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Find an issue on the TensorFlow GitHub repository
- Fix the issue and submit a pull request
- Review the code and provide feedback to other contributors
Career center
Learners who complete Serving Tensorflow Models with a REST API will develop knowledge and skills
that may be useful to these careers:
Software Engineer
Software Engineer
Software Engineers are responsible for researching, developing, and testing software. They write and maintain the code that makes software programs work. This course can help Software Engineers to learn how to serve Tensorflow models with a REST API, which is a valuable skill in the field of software development.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for developing and implementing machine learning models. They work with data scientists to identify and solve business problems using machine learning. This course can help Machine Learning Engineers to learn how to serve Tensorflow models with a REST API, which is a valuable skill in the field of machine learning.
Data Scientist
Data Scientist
Data Scientists are responsible for collecting, cleaning, and analyzing data to extract insights. They use their findings to solve business problems and make informed decisions. This course can help Data Scientists to learn how to serve Tensorflow models with a REST API, which is a valuable skill in the field of data science.
DevOps Engineer
DevOps Engineer
DevOps Engineers are responsible for bridging the gap between development and operations teams. They work to ensure that software is deployed and maintained efficiently. This course can help DevOps Engineers to learn how to serve Tensorflow models with a REST API, which is a valuable skill in the field of DevOps.
Cloud Architect
Cloud Architect
Cloud Architects are responsible for designing and implementing cloud computing solutions. They work with clients to identify their needs and develop solutions that meet their requirements. This course can help Cloud Architects to learn how to serve Tensorflow models with a REST API, which is a valuable skill in the field of cloud computing.
Full-Stack Developer
Full-Stack Developer
Full Stack Developers are responsible for developing both the front-end and back-end of software applications. They work with a variety of technologies to create and maintain software products. This course can help Full Stack Developers to learn how to serve Tensorflow models with a REST API, which is a valuable skill in the field of software development.
Front-End Developer
Front-End Developer
Front-End Developers are responsible for developing the user interface of software applications. They work with designers to create and maintain the look and feel of software products. This course may be useful for Front-End Developers who are interested in learning more about serving Tensorflow models with a REST API.
Back-End Developer
Back-End Developer
Back-End Developers are responsible for developing the server-side of software applications. They work with databases and other technologies to create and maintain the functionality of software products. This course may be useful for Back-End Developers who are interested in learning more about serving Tensorflow models with a REST API.
Database Administrator
Database Administrator
Database Administrators are responsible for managing and maintaining databases. They work with database software and hardware to ensure that data is stored and accessed efficiently. This course may be useful for Database Administrators who are interested in learning more about serving Tensorflow models with a REST API.
System Administrator
System Administrator
System Administrators are responsible for managing and maintaining computer systems. They work with hardware and software to ensure that systems are running smoothly and efficiently. This course may be useful for System Administrators who are interested in learning more about serving Tensorflow models with a REST API.
Network Administrator
Network Administrator
Network Administrators are responsible for managing and maintaining computer networks. They work with hardware and software to ensure that networks are running smoothly and efficiently. This course may be useful for Network Administrators who are interested in learning more about serving Tensorflow models with a REST API.
Security Analyst
Security Analyst
Security Analysts are responsible for protecting computer systems from security threats. They work with software and hardware to identify and mitigate security risks. This course may be useful for Security Analysts who are interested in learning more about serving Tensorflow models with a REST API.
IT Manager
IT Manager
IT Managers are responsible for managing and directing IT departments. They work with other managers to ensure that IT systems are aligned with business goals. This course may be useful for IT Managers who are interested in learning more about serving Tensorflow models with a REST API.
CIO
CIO
CIOs are responsible for leading IT organizations. They work with other executives to ensure that IT is aligned with business strategy. This course may be useful for CIOs who are interested in learning more about serving Tensorflow models with a REST API.
CTO
CTO
CTOs are responsible for leading technology organizations. They work with other executives to ensure that technology is aligned with business strategy. This course may be useful for CTOs who are interested in learning more about serving Tensorflow models with a REST API.
For more career information including salaries, visit:
OpenCourser.com/course/5irz72/serving
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
Serving Tensorflow Models with a REST API.
This second edition of the popular Deep Learning with Python has been revised to include the latest developments and best practices in the field of deep learning.
This hands-on guide to deep learning with Python is widely used by academic institutions and industry professionals, this user-friendly text provides thorough coverage of neural networks, deep learning techniques, and best practices.
Is ideal for those who want to gain a deep understanding of the theoretical underpinnings of deep learning.
This easy-to-follow, comprehensive guide is helpful for learners new to Tensorflow and can be used as a handy handbook once learners have completed the course.
A more in-depth examination of machine learning, this text covers the mechanics of machine learning, common algorithms and models, and practical implementation.
This highly visual book provides a comprehensive overview of deep learning, making it a great choice for beginners.
Focuses on computer vision applications using TensorFlow 2.0.
Provides an in-depth look at probabilistic machine learning, which subfield of machine learning that uses probability theory to model data.
For those interested in reinforcement learning, this book provides a comprehensive introduction to the topic.
Save
TensorFlow can be used with other languages besides Python and this book provides detailed explanations of how to use R with TensorFlow.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/5irz72/serving
Share
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