We may earn an affiliate commission when you visit our partners.
Amit Yadav
In this 2-hour long project-based course, you will learn how to deploy TensorFlow models using TensorFlow Serving and Docker, and you will create a simple web application with Flask which will serve as an interface to get predictions from the served TensorFlow model.
Read more
In this 2-hour long project-based course, you will learn how to deploy TensorFlow models using TensorFlow Serving and Docker, and you will create a simple web application with Flask which will serve as an interface to get predictions from the served TensorFlow model.
Read more
In this 2-hour long project-based course, you will learn how to deploy TensorFlow models using TensorFlow Serving and Docker, and you will create a simple web application with Flask which will serve as an interface to get predictions from the served TensorFlow model.
This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your Internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with (e.g. Python, Jupyter, and Tensorflow) pre-installed.
Prerequisites:
In order to be successful in this project, you should be familiar with Python, TensorFlow, Flask, and HTML.
Notes:
- You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want.
- 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/tip7cn/deploy
What's inside
Syllabus
Deploy Models with TensorFlow Serving and Flask
Welcome to this project-based course on Deploying Models with TensorFlow Serving and Flask. In this project, we will deploy a TensorFlow model with the help of TensorFlow Serving, and we will create a small web app which will serve as a visual interface to the model inference. TensorFlow Serving is the ideal way to serve TensorFlow models in production, and Flask is a minimal web framework which lets developers create web apps really quickly.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches TensorFlow Serving and Flask, two tools that are essential for production model deployment
Covers the entire lifecycle of model development and deployment
Provides hands-on experience via Google's Rhyme platform
Requires knowledge of Python, TensorFlow, and HTML
Works best for learners in the North America region
Save this course
Save Deploy Models with TensorFlow Serving and Flask to your list so you can find it easily later:
Save
Save
Reviews summary
Tensorflow serving and flask basic deployment course
Learners say this basic course on deploying models with TensorFlow Serving and Flask is a good starting point for beginners looking to get into this field. Students describe lectures as easy to follow and well-paced, and they especially enjoyed the hands-on project. However, some learners express that information on the topics could be more in-depth, and they would have liked to see more coverage of TensorFlow Serving.
Beginner-friendly
"Nice way to get started with model deployment with web app."
"Great hands-on tutorial for beginners"
Features a hands-on project
"EXCELLENT"
"Great hands-on tutorial for beginners"
"really good effort to breakdown the complete process and make it easy to understand"
Virtual machine has limitations
"Time given for the virtual desktop is not enought if you actually type and try everything he does."
"The virtual machine could be properly configured so as not to waste time on problems that arise."
"Without Jupiter Netbook it's rather hard to follow."
Lacks in-depth coverage
"Only one video demo with no relation to real-life application"
"I am afriad this presentation really doent work as a format"
"The course is too basic"
"As a potential for improvement I would like to propose more coverage of TensorFlow Service itself"
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 Deploy Models with TensorFlow Serving and Flask with these
activities:
Review Python Fundamentals
Show steps
Brush up on the basics of Python to enhance your understanding of the course material.
Browse courses on
Python Basics
Show steps
-
Review Python syntax and data structures
-
Practice writing simple Python programs
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Show steps
Gain additional insights by reading a relevant book on machine learning with TensorFlow.
View
Hands-On Machine Learning with Scikit-Learn,...
on Amazon
Show steps
-
Read chapters relevant to the course material
Course Material Notes Compilation
Show steps
Improve your understanding by compiling and reviewing key course notes.
Show steps
-
Gather and organize course notes, slides, and readings
-
Review and summarize the compiled materials
Four other activities
Expand to see all activities and additional details
Show all seven activities
Peer Study Group Discussions
Show steps
Engage with peers to discuss course concepts, share knowledge, and offer support.
Show steps
-
Join or create a peer study group
-
Actively participate in group discussions
TensorFlow Serving and Docker Tutorials
Show steps
Enhance your understanding by following guided tutorials on TensorFlow Serving and Docker.
Browse courses on
TensorFlow Serving
Show steps
-
Complete a TensorFlow Serving tutorial
-
Complete a Docker tutorial
TensorFlow Model Deployment Blog Post
Show steps
Solidify your understanding by creating a blog post that explains how to deploy TensorFlow models.
Show steps
-
Research and gather information on TensorFlow model deployment
-
Write a comprehensive blog post covering the deployment process
TensorFlow Model Deployment Project
Show steps
Apply your skills by deploying a TensorFlow model as part of a larger project.
Show steps
-
Define the project scope and objectives
-
Gather and prepare the necessary data
-
Train and deploy the TensorFlow model
-
Evaluate the performance of the deployed model
Review Python Fundamentals
Show steps
Brush up on the basics of Python to enhance your understanding of the course material.
Browse courses on
Python Basics
Show steps
Show steps
Show steps
- Review Python syntax and data structures
- Practice writing simple Python programs
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Show steps
Gain additional insights by reading a relevant book on machine learning with TensorFlow.
View
Hands-On Machine Learning with Scikit-Learn,...
on Amazon
Show steps
Show steps
Show steps
- Read chapters relevant to the course material
Course Material Notes Compilation
Show steps
Improve your understanding by compiling and reviewing key course notes.
Show steps
Show steps
Show steps
- Gather and organize course notes, slides, and readings
- Review and summarize the compiled materials
Four other activities
Expand to see all activities and additional details
Show all seven activities
Peer Study Group Discussions
Show steps
Engage with peers to discuss course concepts, share knowledge, and offer support.
Show steps
Show steps
Show steps
- Join or create a peer study group
- Actively participate in group discussions
TensorFlow Serving and Docker Tutorials
Show steps
Enhance your understanding by following guided tutorials on TensorFlow Serving and Docker.
Browse courses on
TensorFlow Serving
Show steps
Show steps
Show steps
- Complete a TensorFlow Serving tutorial
- Complete a Docker tutorial
TensorFlow Model Deployment Blog Post
Show steps
Solidify your understanding by creating a blog post that explains how to deploy TensorFlow models.
Show steps
Show steps
Show steps
- Research and gather information on TensorFlow model deployment
- Write a comprehensive blog post covering the deployment process
TensorFlow Model Deployment Project
Show steps
Apply your skills by deploying a TensorFlow model as part of a larger project.
Show steps
Show steps
Show steps
- Define the project scope and objectives
- Gather and prepare the necessary data
- Train and deploy the TensorFlow model
- Evaluate the performance of the deployed model
Career center
Learners who complete Deploy Models with TensorFlow Serving and Flask will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
TensorFlow Serving and Flask are essential tools for machine learning engineers who want to deploy machine learning models into production. This course will teach you how to use these technologies to create a web application that can serve predictions from your TensorFlow models. As a machine learning engineer, you will need to be able to deploy models into production in order to make them useful to end users. This course will give you the skills you need to do that.
Data Scientist
Data Scientist
TensorFlow Serving and Flask are essential tools for data scientists who want to deploy machine learning models into production. This course will teach you how to use these technologies to create a web application that can serve predictions from your TensorFlow models. As a data scientist, you will need to be able to deploy models into production in order to make them useful to end users. This course will give you the skills you need to do that.
Software Engineer
Software Engineer
TensorFlow Serving and Flask are essential tools for software engineers who want to deploy machine learning models into production. This course will teach you how to use these technologies to create a web application that can serve predictions from your TensorFlow models. As a software engineer, you will need to be able to deploy models into production in order to make them useful to end users. This course will give you the skills you need to do that.
Data Engineer
Data Engineer
TensorFlow Serving and Flask are essential tools for data engineers who want to deploy machine learning models into production. This course will teach you how to use these technologies to create a web application that can serve predictions from your TensorFlow models. As a data engineer, you will need to be able to deploy models into production in order to make them useful to end users. This course will give you the skills you need to do that.
Data Analyst
Data Analyst
TensorFlow Serving and Flask are essential tools for data analysts who want to deploy machine learning models into production. This course will teach you how to use these technologies to create a web application that can serve predictions from your TensorFlow models. As a data analyst, you will need to be able to deploy models into production in order to make them useful to end users. This course will give you the skills you need to do that.
Web Developer
Web Developer
TensorFlow Serving and Flask are essential tools for web developers who want to deploy machine learning models into production. This course will teach you how to use these technologies to create a web application that can serve predictions from your TensorFlow models. As a web developer, you will need to be able to deploy models into production in order to make them useful to end users. This course will give you the skills you need to do that.
DevOps Engineer
DevOps Engineer
TensorFlow Serving and Flask are essential tools for DevOps engineers who want to deploy machine learning models into production. This course will teach you how to use these technologies to create a web application that can serve predictions from your TensorFlow models. As a DevOps engineer, you will need to be able to deploy models into production in order to make them useful to end users. This course will give you the skills you need to do that.
Cloud Architect
Cloud Architect
TensorFlow Serving and Flask are essential tools for cloud architects who want to deploy machine learning models into production. This course will teach you how to use these technologies to create a web application that can serve predictions from your TensorFlow models. As a cloud architect, you will need to be able to deploy models into production in order to make them useful to end users. This course will give you the skills you need to do that.
Product Manager
Product Manager
TensorFlow Serving and Flask are essential tools for product managers who want to deploy machine learning models into production. This course will teach you how to use these technologies to create a web application that can serve predictions from your TensorFlow models. As a product manager, you will need to be able to deploy models into production in order to make them useful to end users. This course will give you the skills you need to do that.
Data Visualization Engineer
Data Visualization Engineer
TensorFlow Serving and Flask are essential tools for data visualization engineers who want to deploy machine learning models into production. This course will teach you how to use these technologies to create a web application that can serve predictions from your TensorFlow models. As a data visualization engineer, you will need to be able to deploy models into production in order to make them useful to end users. This course will give you the skills you need to do that.
Business Analyst
Business Analyst
TensorFlow Serving and Flask are essential tools for business analysts who want to deploy machine learning models into production. This course will teach you how to use these technologies to create a web application that can serve predictions from your TensorFlow models. As a business analyst, you will need to be able to deploy models into production in order to make them useful to end users. This course will give you the skills you need to do that.
UX Designer
UX Designer
TensorFlow Serving and Flask are essential tools for UX designers who want to deploy machine learning models into production. This course will teach you how to use these technologies to create a web application that can serve predictions from your TensorFlow models. As a UX designer, you will need to be able to deploy models into production in order to make them useful to end users. This course will give you the skills you need to do that.
Technical Writer
Technical Writer
TensorFlow Serving and Flask are essential tools for technical writers who want to deploy machine learning models into production. This course will teach you how to use these technologies to create a web application that can serve predictions from your TensorFlow models. As a technical writer, you will need to be able to deploy models into production in order to make them useful to end users. This course will give you the skills you need to do that.
IT Support Specialist
IT Support Specialist
TensorFlow Serving and Flask are essential tools for IT support specialists who want to deploy machine learning models into production. This course will teach you how to use these technologies to create a web application that can serve predictions from your TensorFlow models. As an IT support specialist, you will need to be able to deploy models into production in order to make them useful to end users. This course will give you the skills you need to do that.
Teacher
Teacher
TensorFlow Serving and Flask are essential tools for teachers who want to deploy machine learning models into production. This course will teach you how to use these technologies to create a web application that can serve predictions from your TensorFlow models. As a teacher, you will need to be able to deploy models into production in order to make them useful to your students. This course will give you the skills you need to do that.
For more career information including salaries, visit:
OpenCourser.com/course/tip7cn/deploy
Reading list
We've selected six 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
Deploy Models with TensorFlow Serving and Flask.
Provides a comprehensive guide to using Flask, a Python web framework for developing web applications. It covers the basic concepts of Flask, as well as more advanced topics such as authentication, authorization, and database integration.
Provides a comprehensive introduction to deep learning using Python. It covers the basics of deep learning, as well as more advanced topics such as convolutional neural networks and recurrent neural networks.
Provides a collection of hands-on projects using TensorFlow for machine learning. It covers a variety of topics, including image classification, natural language processing, and time series analysis.
Provides a step-by-step guide to machine learning using Python. It covers the basics of machine learning, as well as more advanced topics such as deep learning and natural language processing.
Provides a comprehensive introduction to natural language processing using Python. It covers the basics of natural language processing, as well as more advanced topics such as machine translation and text classification.
Provides a comprehensive introduction to computer vision using Python. It covers the basics of computer vision, as well as more advanced topics such as object detection and image segmentation.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/tip7cn/deploy
Share
Similar courses
Here are nine courses similar to
Deploy Models with TensorFlow Serving and Flask.
Image Classification with CNNs using Keras
OpenCourser.com/course/mmno5z/image
Image Classification with CNNs using Keras
Most relevant
Classify Radio Signals from Space using Keras
OpenCourser.com/course/k2tego/classify
Classify Radio Signals from Space using Keras
Most relevant
Custom Prediction Routine on Google AI Platform
OpenCourser.com/course/0x3o44/custom
Custom Prediction Routine on Google AI Platform
Most relevant
Computer Vision - Object Tracking with OpenCV and Python
OpenCourser.com/course/k3ogrv/computer
Computer Vision - Object Tracking with OpenCV and Python
Most relevant
Video Basics with OpenCV and Python
OpenCourser.com/course/mhrkdn/video
Video Basics with OpenCV and Python
Most relevant
Support Vector Machines in Python, From Start to Finish
OpenCourser.com/course/gqhjy9/support
Support Vector Machines in Python, From Start to Finish
Most relevant
Image Super Resolution Using Autoencoders in Keras
OpenCourser.com/course/i9tgr7/image
Image Super Resolution Using Autoencoders in Keras
Most relevant
Anomaly Detection in Time Series Data with Keras
OpenCourser.com/course/ax5jib/anomaly
Anomaly Detection in Time Series Data with Keras
Most relevant
Create Custom Layers in Keras
OpenCourser.com/course/lxu3gd/create
Create Custom Layers in Keras
Most relevant
Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Amit Yadav
Language
English
Good to know
Teaches TensorFlow Serving and Flask, two tools that are essential for production model deployment
Covers the entire lifecycle of model development and deployment
Provides hands-on experience via Google's Rhyme platform
Requires knowledge of Python, TensorFlow, and HTML
Works best for learners in the North America region
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2024 OpenCourser