This course is part of the TensorFlow: Data and Deployment Specialization

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stars
441 ratings

Laurence Moroney

17,756 already enrolled

Offered By

TensorFlow: Data and Deployment SpecializationDeepLearning.AI

About this Course

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Bringing a machine learning model into the real world involves a lot more than just modeling. This Specialization will teach you how to navigate various deployment scenarios and use data more effectively to train your model.
In this final course, you’ll explore four different scenarios you’ll encounter when deploying models. You’ll be introduced to TensorFlow Serving, a technology that lets you do inference over the web. You’ll move on to TensorFlow Hub, a repository of models that you can use for transfer learning. Then you’ll use TensorBoard to evaluate and understand how your models work, as well as share your model metadata with others. Finally, you’ll explore federated learning and how you can retrain deployed models with user data while maintaining data privacy.

This Specialization builds upon our TensorFlow in Practice Specialization. If you are new to TensorFlow, we recommend that you take the TensorFlow in Practice Specialization first. To develop a deeper, foundational understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 4 of 4 in the

TensorFlow: Data and Deployment Specialization

Intermediate Level

We recommend taking Course 1 of the TensorFlow in Practice Specialization first, or have basic familiarity with building models in TensorFlow.

Approx. 13 hours to complete

English

What you will learn

Use TensorFlow Serving to do inference over the web
Navigate TensorFlow Hub, a repository of models that you can use for transfer learning
Evaluate how your models work and share model metadata using TensorBoard
Explore federated learning and how to retrain deployed models while maintaining data privacy

Skills you will gain

TensorFlow Serving
Machine Learning
federated learning
TensorFlow Hub
TensorBoard

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 4 of 4 in the

TensorFlow: Data and Deployment Specialization

Intermediate Level

We recommend taking Course 1 of the TensorFlow in Practice Specialization first, or have basic familiarity with building models in TensorFlow.

Approx. 13 hours to complete

English

Instructor

Instructor rating

4.9/5 (42 Ratings)

Laurence Moroney

Instructor

Lead AI Advocate, Google

417,865 Learners

15 Courses

Offered by

DeepLearning.AI

Syllabus - What you will learn from this course

Content Rating97%(1,151 ratings)

Week1

Week 1

1 hour to complete

TensorFlow Extended

1 hour to complete

12 videos (Total 21 min), 5 readings, 3 quizzes

12 videos

Introduction, A conversation with Andrew Ng2mIntroduction24sServing3mInstalling TF Serving1mTensorFlow Serving summary30sSetup for serving2mServing1mPredictions41sPassing data to serving1mGetting the predictions back1mRunning the colab2mComplex model2m

5 readings

Downloading the Ungraded Labs and Programming Assignments10mInstallation link10mTF server running in colab10mServing with Fashion MNIST10mUngraded Assignment - Serving with MNIST10m

1 practice exercise

Week 1 Quiz

Week2

Week 2

6 hours to complete

Sharing pre-trained models with TensorFlow Hub

6 hours to complete

11 videos (Total 20 min), 7 readings, 3 quizzes

11 videos

Introduction, A conversation with Andrew Ng2mIntroduction to TF Hub2mTransfer learning1mInference1mModule storage2mText based models1mWord embeddings1mExperimenting with embeddings1mColab1mClassify cats and dogs1mTransfer learning1m

7 readings

Tensorflow Hub link10mLink to saved models10mColab10mPre-trained Word Embeddings10mText Classification Colab10mMobileNet model details10mColab10m

1 practice exercise

Week 2 Quiz

Week3

Week 3

5 hours to complete

Tensorboard: tools for model training

5 hours to complete

10 videos (Total 16 min), 2 readings, 3 quizzes

Week4

Week 4

1 hour to complete

Federated Learning

1 hour to complete

9 videos (Total 22 min), 3 readings, 2 quizzes

Reviews

4.8

56 reviews

5 stars
81.44%
4 stars
14.25%
3 stars
2.94%
2 stars
0.90%
1 star
0.45%

TOP REVIEWS FROM ADVANCED DEPLOYMENT SCENARIOS WITH TENSORFLOW

by NBJul 29, 2022

Excellent Content. It Will definitely Advance my career.

by MSJul 4, 2020

great course for utilities to enhance the training and deployment experience

by ASMay 15, 2020

Awesome course for the using and application of Machine learning

by JSNov 9, 2021

Grader messages are not helpful due to which debugging time increases. Rest the course is quite informational and useful.

View all reviews

About the TensorFlow: Data and Deployment Specialization

Continue developing your skills in TensorFlow as you learn to navigate through a wide range of deployment scenarios and discover new ways to use data more effectively when training your machine learning models.

In this four-course Specialization, you’ll learn how to get your machine learning models into the hands of real people on all kinds of devices. Start by understanding how to train and run machine learning models in browsers and in mobile applications. Learn how to leverage built-in datasets with just a few lines of code, learn about data pipelines with TensorFlow data services, use APIs to control data splitting, process all types of unstructured data, and retrain deployed models with user data while maintaining data privacy. Apply your knowledge in various deployment scenarios and get introduced to TensorFlow Serving, TensorFlow, Hub, TensorBoard, and more. Industries all around the world are adopting Artificial Intelligence. This Specialization from Laurence Moroney and Andrew Ng will help you develop and deploy machine learning models across any device or platform faster and more accurately than ever. Looking for a place to start? Master the foundational basics of TensorFlow with the DeepLearning.AI TensorFlow Developer Professional Certificate. Looking to customize and build powerful real-world models for complex scenarios? Check out the TensorFlow: Advanced Techniques Specialization.

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

More questions? Visit the Learner Help Center.

Advanced Deployment Scenarios with TensorFlow

About this Course

What you will learn

Skills you will gain