Data Pipelines with TensorFlow Data Services

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

4.1
stars
269 ratings

Laurence Moroney

Offered By

TensorFlow: Data and Deployment SpecializationDeepLearning.AI

About this Course

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.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 3 of 4 in the

TensorFlow: Data and Deployment Specialization

Flexible deadlines

Reset deadlines in accordance to your schedule.

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

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

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 NetworkTensorflowExtraction, Transformation And Loading (ETL)Data PipelinesTensorFlow Datasets

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 3 of 4 in the

TensorFlow: Data and Deployment Specialization

Flexible deadlines

Reset deadlines in accordance to your schedule.

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

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

Instructor

Instructor rating

4.73/5 (19 Ratings)

Laurence Moroney

Instructor

Lead AI Advocate, Google

288,644 Learners

14 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

Week

Week 1

3 hours to complete

Data Pipelines with TensorFlow Data Services

This week, you will be able to perform efficient ETL tasks using Tensorflow Data Services APIs

3 hours to complete

10 videos (Total 21 min), 4 readings, 2 quizzes

10 videos

A conversation with Andrew Ng2m

Introduction1m

Popular Datasets2m

Data Pipelines58s

Extract, Transform and Load3m

Versioning Datasets2m

Looking at the Notebook1m

Using TFDS in Keras to Train Fashion MNIST 3m

Horses or Humans in TFDS2m

Week 1 Wrap Up49s

4 readings

Downloading the Coding Examples and Exercises10m

Try Out the Notebook Yourself10m

Try the Horses or Human Notebook10m

Grader Note10m

1 practice exercise

Week 1 Quiz30m

Week

Week 2

2 hours to complete

Splits and Slices API for Datasets in TF

In this week, you will construct train/validation/test splits of any dataset - either custom or present in TensorFlow hub dataset library - using Splits API

2 hours to complete

7 videos (Total 26 min), 3 readings, 2 quizzes

Week

Week 3

3 hours to complete

Exporting Your Data into the Training Pipeline

This week you will extend your knowledge of data pipelines

3 hours to complete

21 videos (Total 44 min), 5 readings, 2 quizzes

21 videos

A Conversation with Andrew Ng1m

Introduction22s

Input Data1m

Basic Mechanics2m

Numeric and Bucketized Columns2m

Vocabulary and Hashed Columns, Feature Crossing2m

Embedding Columns2m

Introduction24s

Notebook Walkthrough4m

Introduction19s

Numpy, Pandas and Images2m

CSV3m

Text and TFRecord1m

Generators1m

Introduction17s

Notebook walkthrough4m

Introduction1m

Using Numpy and Pandas2m

Image Data1m

CSV Data4m

Text Data2m

5 readings

Link to the Notebook10m

Link to the CNN Course10m

Link to the Notebook10m

CSV Colab10m

Link to the Course 10m

1 practice exercise

Week 3 Quiz30m

Week

Week 4

3 hours to complete

Performance

You'll learn how to handle your data input to avoid bottlenecks, race conditions and more!

3 hours to complete

22 videos (Total 44 min), 2 readings, 3 quizzes

22 videos

A conversation with Andrew Ng2m

Introduction36s

ETL2m

What Happens When You Train a Model2m

Introduction25s

Caching58s

Parallelism APIs2m

Autotuning2m

Parallelizing Data Extraction2m

Best Practices for Code Improvements3m

A Few Words by Laurence34s

A conversation with Andrew Ng1m

Introduction44s

How to Start Using a Dataset2m

Implementation4m

File Access and Possible Problems in Data3m

Publishing the Dataset3m

Introduction18s

Going Through the Colab- Part 12m

Going Through the Colab - Part 22m

Closing Words14s

A conversation with Andrew Ng1m

2 readings

URLs10m

Link to the Colab10m

2 practice exercises

Week 4 Quiz

Publishing your Dataset Quiz

Reviews

4.1

74 reviews

5 stars
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4 stars
25.18%
3 stars
13.33%
2 stars
6.66%
1 star
4.44%

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 PCApr 16, 2020

I understand why most of the students are furious about, but content wise, it one of those extremely helpful and important courses in Coursera. Really loved it!

by MIApr 3, 2020

This course is quite essential, yet it is prepared too quickly? I had a hard time on Week 3 and Week 4 because the exercises are hard to grasp.

by GLMar 2, 2020

Laurence cares deeply about the students. Not only about what they learn, but that they actually enjoy and learn it. What a fantastic teacher.

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, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.
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.
Will I earn university credit for completing the Course?
This Course doesn't carry university credit, but some universities may choose to accept Course Certificates for credit. Check with your institution to learn more. Online Degrees and Mastertrack™ Certificates on Coursera provide the opportunity to earn university credit.

More questions? Visit the Learner Help Center.

Data Pipelines with TensorFlow Data Services