Data Pipelines with TensorFlow Data Services
- 4.3
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Approx. 11 hours to complete
Course Summary
Learn how to build scalable data pipelines using TensorFlow and Apache Beam. This course covers important concepts such as data processing, batch and stream processing, and data modeling.Key Learning Points
- Explore the fundamentals of data pipelines and their importance for scalable data analysis
- Learn how to use TensorFlow and Apache Beam to build data pipelines
- Understand the difference between batch and stream processing and how to use both effectively
Job Positions & Salaries of people who have taken this course might have
- USA: $92,000 - $137,000
- India: INR 700,000 - INR 1,000,000
- Spain: €30,000 - €50,000
- USA: $92,000 - $137,000
- India: INR 700,000 - INR 1,000,000
- Spain: €30,000 - €50,000
- USA: $106,000 - $163,000
- India: INR 800,000 - INR 1,500,000
- Spain: €35,000 - €60,000
- USA: $92,000 - $137,000
- India: INR 700,000 - INR 1,000,000
- Spain: €30,000 - €50,000
- USA: $106,000 - $163,000
- India: INR 800,000 - INR 1,500,000
- Spain: €35,000 - €60,000
- USA: $110,000 - $155,000
- India: INR 850,000 - INR 1,200,000
- Spain: €40,000 - €70,000
Related Topics for further study
Learning Outcomes
- Build scalable data pipelines using TensorFlow and Apache Beam
- Effectively process batch and stream data
- Understand the importance of data modeling in scalable data analysis
Prerequisites or good to have knowledge before taking this course
- Basic understanding of Python programming
- Familiarity with data analysis and processing concepts
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
Similar Courses
- Data Engineering, Big Data, and Machine Learning on GCP
- Data Engineering with Google Cloud
Related Education Paths
Notable People in This Field
- Martin Gorner
- Maximilian Schmitt
Related Books
Description
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.
Knowledge
- 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
Outline
- Data Pipelines with TensorFlow Data Services
- A conversation with Andrew Ng
- Introduction
- Popular Datasets
- Data Pipelines
- Extract, Transform and Load
- Versioning Datasets
- Looking at the Notebook
- Using TFDS in Keras to Train Fashion MNIST
- Horses or Humans in TFDS
- Week 1 Wrap Up
- Downloading the Ungraded Labs and Programming Assignments
- Try Out the Notebook Yourself
- Try the Horses or Human Notebook
- Grader Note
- Week 1 Quiz
- Splits and Slices API for Datasets in TF
- Introduction
- Introduction to Splits API
- Splits API Notebook Walkthrough
- File Structure in TensorFlow Datasets
- Feature Descriptors
- TFRecord Colab Walkthrough
- Week 2 Wrap Up
- Splits API Notebook
- TFRecord Notebook
- Grader Note
- Week 2
- Exporting Your Data into the Training Pipeline
- A Conversation with Andrew Ng
- Introduction
- Input Data
- Basic Mechanics
- Numeric and Bucketized Columns
- Vocabulary and Hashed Columns, Feature Crossing
- Embedding Columns
- Introduction
- Notebook Walkthrough
- Introduction
- Numpy, Pandas and Images
- CSV
- Text and TFRecord
- Generators
- Introduction
- Notebook walkthrough
- Introduction
- Using Numpy and Pandas
- Image Data
- CSV Data
- Text Data
- Link to the Notebook
- Link to the CNN Course
- Link to the Notebook
- CSV Notebook
- Link to the Course
- Week 3 Quiz
- Performance
- A conversation with Andrew Ng
- Introduction
- ETL
- What Happens When You Train a Model
- Introduction
- Caching
- Parallelism APIs
- Autotuning
- Parallelizing Data Extraction
- Best Practices for Code Improvements
- A Few Words by Laurence
- A conversation with Andrew Ng
- Introduction
- How to Start Using a Dataset
- Implementation
- File Access and Possible Problems in Data
- Publishing the Dataset
- Introduction
- Going Through the Colab- Part 1
- Going Through the Colab - Part 2
- Closing Words
- A conversation with Andrew Ng
- URLs
- Link to the Colab
Summary of User Reviews
Learn about data pipelines with TensorFlow on Coursera. Users have given positive reviews for this course, praising its comprehensive coverage of the topic.Key Aspect Users Liked About This Course
Comprehensive coverage of data pipelines with TensorFlow.Pros from User Reviews
- In-depth explanations and practical examples provided.
- Course content is well-organized and easy to follow.
- Instructor is knowledgeable and engaging.
- Great resource for those interested in machine learning and data engineering.
- Course exercises are challenging and rewarding.
Cons from User Reviews
- Some users found the course too technical and difficult to understand.
- Course may be too basic for advanced learners.
- Lack of hands-on projects and real-world applications.
- Course may not be suitable for those without prior knowledge of TensorFlow.
- Some users experienced technical issues with the platform.
Laurence Moroney
4.3 Raiting
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