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

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Laurence Moroney

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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 third course, you will:
- Perform streamlined ETL tasks using TensorFlow Data Services
- Load different datasets and custom feature vectors using TensorFlow Hub and TensorFlow Data Services APIs
- Create and use pre-built pipelines for generating highly reproducible I/O pipelines for any dataset
- Optimize data pipelines that become a bottleneck in the training process
- Publish your own datasets to the TensorFlow Hub library and share standardized data with researchers and developers around the world


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

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Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 3 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 a basic familiarity with building models in TensorFlow

Approx. 11 hours to complete

English

What you will learn

Perform efficient ETL tasks using Tensorflow Data Services APIs
Construct train/validation/test splits of any dataset - either custom or present in TensorFlow Hub Dataset library - using Splits API
Use different modules and functions of the TFDS API to prepare your data for training pipelines
Identify bottlenecks in your input pipelines and increase your workflow efficiency by input parallelization

Skills you will gain

Artificial Neural Network
Tensorflow
Extraction, Transformation And Loading (ETL)
Data Pipelines
TensorFlow Datasets

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 3 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 a basic familiarity with building models in TensorFlow

Approx. 11 hours to complete

English

Instructor

Instructor rating

4.84/5 (44 Ratings)

Laurence Moroney

Instructor

Lead AI Advocate, Google

416,486 Learners

15 Courses

Offered by

DeepLearning.AI

Syllabus - What you will learn from this course

Content Rating92%(1,333 ratings)

Week1

Week 1

3 hours to complete

Data Pipelines with TensorFlow Data Services

3 hours to complete

10 videos (Total 21 min), 4 readings, 4 quizzes

10 videos

A conversation with Andrew Ng2mIntroduction1mPopular Datasets2mData Pipelines58sExtract, Transform and Load3mVersioning Datasets2mLooking at the Notebook1mUsing TFDS in Keras to Train Fashion MNIST 3mHorses or Humans in TFDS2mWeek 1 Wrap Up49s

4 readings

Downloading the Ungraded Labs and Programming Assignments10mTry Out the Notebook Yourself10mTry the Horses or Human Notebook10mGrader Note10m

1 practice exercise

Week 1 Quiz30m

Week2

Week 2

3 hours to complete

Splits and Slices API for Datasets in TF

3 hours to complete

7 videos (Total 26 min), 3 readings, 3 quizzes

Week3

Week 3

3 hours to complete

Exporting Your Data into the Training Pipeline

3 hours to complete

21 videos (Total 44 min), 5 readings, 3 quizzes

21 videos

A Conversation with Andrew Ng1mIntroduction22sInput Data1mBasic Mechanics2mNumeric and Bucketized Columns2mVocabulary and Hashed Columns, Feature Crossing2mEmbedding Columns2mIntroduction24sNotebook Walkthrough4mIntroduction19sNumpy, Pandas and Images2mCSV3mText and TFRecord1mGenerators1mIntroduction17sNotebook walkthrough4mIntroduction1mUsing Numpy and Pandas2mImage Data1mCSV Data4mText Data2m

5 readings

Link to the Notebook10mLink to the CNN Course10mLink to the Notebook10mCSV Notebook10mLink to the Course 10m

1 practice exercise

Week 3 Quiz30m

Week4

Week 4

3 hours to complete

Performance

3 hours to complete

22 videos (Total 44 min), 2 readings, 4 quizzes

22 videos

A conversation with Andrew Ng2mIntroduction36sETL2mWhat Happens When You Train a Model2mIntroduction25sCaching58sParallelism APIs2mAutotuning2mParallelizing Data Extraction2mBest Practices for Code Improvements3mA Few Words by Laurence34sA conversation with Andrew Ng1mIntroduction44sHow to Start Using a Dataset2mImplementation4mFile Access and Possible Problems in Data3mPublishing the Dataset3mIntroduction18sGoing Through the Colab- Part 12mGoing Through the Colab - Part 22mClosing Words14sA conversation with Andrew Ng1m

2 readings

URLs10mLink to the Colab10m

2 practice exercises

Week 4 QuizPublishing your Dataset Quiz

Reviews

4.4

96 reviews

5 stars
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4 stars
19.87%
3 stars
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2 stars
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1 star
2.59%

TOP REVIEWS FROM DATA PIPELINES WITH TENSORFLOW DATA SERVICES

by YOMay 16, 2020

Debugging exercises due to errors in indentation sounded stupid in the first place. But the joy of finally getting a "yes" in the assignment auto-grader beats them all.

by NPMay 10, 2020

Great Experience of Learning and finally came to know in more depth about Tensorflow and definitely I shall use it for future work.

by ADFeb 2, 2020

Excellent course both for Data Scientists and Machine Learning Engineers!

by SZMay 19, 2020

I learned a lot from this course about how to optimize TensorFlow data pipelines and how to create public datasets. Thank you! - Steve

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.
Why was Course 3: Data Pipelines with TensorFlow Data Service updated in October 2020?
Changes in TensorFlow API: Since this Specialization was launched in early 2020, there have been changes to the TensorFlow API which affect the material in Weeks 1 and 2. With this refresh, you can access updated lectures, quizzes, and assignments.
Changing the Difficulty Level of Assignments: Based on valuable learner feedback, we’ve revised the Week 4 assignments to ensure that you have a full grasp of the foundational principles and are well-prepared to tackle them.

More questions? Visit the Learner Help Center.

Data Pipelines with TensorFlow Data Services

About this Course

What you will learn

Skills you will gain