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

4.7
stars
423 ratings
•
51 reviews

Laurence Moroney

16,334 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.

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. 18 hours to complete

English

Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish

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.

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. 18 hours to complete

English

Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish

Instructor

Instructor rating

4.89/5 (39 Ratings)

Laurence Moroney

Instructor

Lead AI Advocate, Google

364,788 Learners

15 Courses

Offered by

DeepLearning.AI

DeepLearning.AI is an education technology company that develops a global community of AI talent.

Syllabus - What you will learn from this course

Content Rating96%(1,045 ratings)

Week

Week 1

3 hours to complete

TensorFlow Extended

3 hours to complete

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

12 videos

Introduction, A conversation with Andrew Ng2m

Introduction24s

Serving3m

Installing TF Serving1m

TensorFlow Serving summary30s

Setup for serving2m

Serving1m

Predictions41s

Passing data to serving1m

Getting the predictions back1m

Running the colab2m

Complex model2m

5 readings

Downloading the Ungraded Labs and Programming Assignments10m

Installation link10m

TF server running in colab10m

Serving with Fashion MNIST10m

Ungraded Assignment - Serving with MNIST10m

1 practice exercise

Week 1 Quiz

Week

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 Ng2m

Introduction to TF Hub2m

Transfer learning1m

Inference1m

Module storage2m

Text based models1m

Word embeddings1m

Experimenting with embeddings1m

Colab1m

Classify cats and dogs1m

Transfer learning1m

7 readings

Tensorflow Hub link10m

Link to saved models10m

Colab10m

Pre-trained Word Embeddings10m

Text Classification Colab10m

MobileNet model details10m

Colab10m

1 practice exercise

Week 2 Quiz

Week

Week 3

6 hours to complete

Tensorboard: tools for model training

6 hours to complete

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

Week

Week 4

2 hours to complete

Federated Learning

2 hours to complete

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

Reviews

4.7

51 reviews

5 stars
81.11%
4 stars
14.45%
3 stars
3.03%
2 stars
0.93%
1 star
0.46%

TOP REVIEWS FROM ADVANCED DEPLOYMENT SCENARIOS WITH TENSORFLOW

by SMApr 10, 2020

This topic seems to be very important but the content is not as good as rest of the weeks content, more explanations and more simple examples could have helped

by ASMay 15, 2020

Awesome course for the using and application of Machine learning

by ASJan 21, 2021

The fact that there were still some problems in the Course regarding technical or exercise based, it shows that this material is relatively new in the domain.

by DBMay 17, 2020

Great work and I highly recommend this course/specialization! Good job of inserting needed edits to update what's happening in real time.

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