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Laurence Moroney
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.
Read more
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.
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/my74u3/data
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What's inside
Syllabus
Data Pipelines with TensorFlow Data Services
This week, you will be able to perform efficient ETL tasks using Tensorflow Data Services APIs
Splits and Slices API for Datasets in TF
Read more
Syllabus
Data Pipelines with TensorFlow Data Services
This week, you will be able to perform efficient ETL tasks using Tensorflow Data Services APIs
Splits and Slices API for Datasets in TF
Read more
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
Exporting Your Data into the Training Pipeline
This week you will extend your knowledge of data pipelines
Performance
You'll learn how to handle your data input to avoid bottlenecks, race conditions and more!
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Deepens theoretical understanding of machine learning modeling by emphasizing real-world applications
Provides practical guidance on deploying machine learning models for real-world use cases
Introduces TensorFlow Data Services, a powerful tool for managing and processing large datasets
Develops skills in creating and using pre-built pipelines for efficient I/O operations with TensorFlow
Offers opportunities to publish datasets to the TensorFlow Hub library, promoting collaboration and data sharing
Builds upon the TensorFlow in Practice Specialization, providing a solid foundation for advanced learners
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Save Data Pipelines with TensorFlow Data Services to your list so you can find it easily later:
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Reviews summary
Tensorflow data services: pipeline construction
learners say that TensorFlow Data Services: Pipeline Construction has engaging assignments and is suitable for beginners with some background in Python and TensorFlow. The lectures are well received, but assignments, especially in the final week, are flagged as being difficult. Despite this, students find the course to be a valuable resource for learning how to build data pipelines using TensorFlow data services.
The instructor is knowledgeable and engaging.
"Laurence cares deeply about the students. Not only about what they learn, but that they actually enjoy and learn it. What a fantastic teacher."
"The course materials were a detail explanation of the data pipelines in TensorFlow."
Course content and lectures are clear and beginner-friendly, though the last week's material is flagged as being rushed.
"This course is an excellent resource for individuals who want to learn about building data pipelines using TensorFlow data services. The course content is well-structured, and the instructors did a fantastic job of explaining complex concepts in a clear and concise manner."
"Really important topics if you want to operationalize your ML models. Final exercise was pretty hard to debug to satisfy automatic grader which produces a lot of frustrations for learners. Should be redesigned."
"The course was nicely built from basics till the end of publishing our own datasets by the instructor Laurence Moroney."
Exercises, especially in the last week, can be challenging to complete and debug.
"Debugging exercises due to errors in indentation sounded stupid in the first place."
"That makes me sad to give such a bad rating, because I'm a big fan of Andrew Ng and DeepLearning.ai courses, but this one is really not at standart.The lectures are confusing, we don't understand what's the goal of all that until week3. The assignments can be a pain to pass, not because your code is wrong, but because you added a newline or modify a bit the cell."
"People have complained about the last assignment's compilation problems for months and it still has not been solved. No teacher answers students' questions in the forums either, so prepare yourself for spending hours reading the forums for the last assignment and resubmitting it till, without learning anything new, you realise there was an extra "s" in the name of a variable during the videos and that was causing the compilation problem."
Assignments can be challenging, with a particular emphasis on the final assignment.
"Debugging exercises due to errors in indentation sounded stupid in the first place."
"That makes me sad to give such a bad rating, because I'm a big fan of Andrew Ng and DeepLearning.ai courses, but this one is really not at standart.The lectures are confusing, we don't understand what's the goal of all that until week3. The assignments can be a pain to pass, not because your code is wrong, but because you added a newline or modify a bit the cell."
"People have complained about the last assignment's compilation problems for months and it still has not been solved. No teacher answers students' questions in the forums either, so prepare yourself for spending hours reading the forums for the last assignment and resubmitting it till, without learning anything new, you realise there was an extra "s" in the name of a variable during the videos and that was causing the compilation problem."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Data Pipelines with TensorFlow Data Services with these
activities:
Review Data Preprocessing Techniques
Show steps
Reviewing data preprocessing techniques will help you understand how to prepare data for machine learning models.
Browse courses on
Data Preprocessing
Show steps
-
Review the different types of data preprocessing techniques.
-
Learn how to apply data preprocessing techniques to real-world data.
Practice Using TensorFlow Data Services
Show steps
Practicing using TensorFlow Data Services will help you become more proficient in using this tool for data preprocessing and loading.
Show steps
-
Create a TensorFlow Data Services dataset.
-
Load data into the dataset.
-
Preprocess the data using TensorFlow Data Services.
Build Real-World Data Pipelines
Show steps
Following online tutorials that focus on building real-world data pipelines will expedite your learning process.
Browse courses on
Data Pipelines
Show steps
-
Find a tutorial that aligns with your learning objectives.
-
Follow the tutorial step-by-step.
-
Implement the pipeline in your own project.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Optimize Data Pipelines
Show steps
Improving your ability to optimize data pipelines should lead to improvements in the overall performance of your machine learning model.
Browse courses on
Performance
Show steps
-
Identify bottlenecks in your data pipeline.
-
Implement techniques to reduce bottlenecks, such as caching and parallelization.
-
Monitor your data pipeline to ensure that it is performing optimally.
Design a Data Pipeline for a Specific Dataset
Show steps
Designing a data pipeline for a specific dataset will help you understand the different components of a data pipeline and how they work together.
Browse courses on
Data Pipelines
Show steps
-
Choose a dataset that you are interested in.
-
Identify the tasks that need to be performed on the data to prepare it for training.
-
Design a data pipeline that performs these tasks.
-
Implement the data pipeline using TensorFlow Data Services.
Collaborate with Peers on a Data Pipeline Project
Show steps
Collaborating with peers on a data pipeline project will allow you to share knowledge, learn from others, and get feedback on your work.
Browse courses on
Data Pipelines
Show steps
-
Find a group of peers who are interested in working on a data pipeline project.
-
Choose a dataset and a set of tasks to be performed on the data.
-
Design and implement a data pipeline that performs these tasks.
-
Present your work to the group.
Attend a Workshop on Data Pipelines
Show steps
Attending a workshop on data pipelines will provide you with an opportunity to learn from experts in the field and get hands-on experience with data pipeline tools.
Browse courses on
Data Pipelines
Show steps
-
Find a workshop that aligns with your learning objectives.
-
Attend the workshop.
-
Participate in the hands-on exercises.
Contribute to an Open-Source Data Pipeline Project
Show steps
Contributing to an open-source data pipeline project will give you the opportunity to work on a real-world project and learn from other developers.
Browse courses on
Data Pipelines
Show steps
-
Find an open-source data pipeline project that you are interested in.
-
Identify an area where you can contribute to the project.
-
Submit a pull request with your contribution.
Review Data Preprocessing Techniques
Show steps
Reviewing data preprocessing techniques will help you understand how to prepare data for machine learning models.
Browse courses on
Data Preprocessing
Show steps
Show steps
Show steps
- Review the different types of data preprocessing techniques.
- Learn how to apply data preprocessing techniques to real-world data.
Practice Using TensorFlow Data Services
Show steps
Practicing using TensorFlow Data Services will help you become more proficient in using this tool for data preprocessing and loading.
Show steps
Show steps
Show steps
- Create a TensorFlow Data Services dataset.
- Load data into the dataset.
- Preprocess the data using TensorFlow Data Services.
Build Real-World Data Pipelines
Show steps
Following online tutorials that focus on building real-world data pipelines will expedite your learning process.
Browse courses on
Data Pipelines
Show steps
Show steps
Show steps
- Find a tutorial that aligns with your learning objectives.
- Follow the tutorial step-by-step.
- Implement the pipeline in your own project.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Optimize Data Pipelines
Show steps
Improving your ability to optimize data pipelines should lead to improvements in the overall performance of your machine learning model.
Browse courses on
Performance
Show steps
Show steps
Show steps
- Identify bottlenecks in your data pipeline.
- Implement techniques to reduce bottlenecks, such as caching and parallelization.
- Monitor your data pipeline to ensure that it is performing optimally.
Design a Data Pipeline for a Specific Dataset
Show steps
Designing a data pipeline for a specific dataset will help you understand the different components of a data pipeline and how they work together.
Browse courses on
Data Pipelines
Show steps
Show steps
Show steps
- Choose a dataset that you are interested in.
- Identify the tasks that need to be performed on the data to prepare it for training.
- Design a data pipeline that performs these tasks.
- Implement the data pipeline using TensorFlow Data Services.
Collaborate with Peers on a Data Pipeline Project
Show steps
Collaborating with peers on a data pipeline project will allow you to share knowledge, learn from others, and get feedback on your work.
Browse courses on
Data Pipelines
Show steps
Show steps
Show steps
- Find a group of peers who are interested in working on a data pipeline project.
- Choose a dataset and a set of tasks to be performed on the data.
- Design and implement a data pipeline that performs these tasks.
- Present your work to the group.
Attend a Workshop on Data Pipelines
Show steps
Attending a workshop on data pipelines will provide you with an opportunity to learn from experts in the field and get hands-on experience with data pipeline tools.
Browse courses on
Data Pipelines
Show steps
Show steps
Show steps
- Find a workshop that aligns with your learning objectives.
- Attend the workshop.
- Participate in the hands-on exercises.
Contribute to an Open-Source Data Pipeline Project
Show steps
Contributing to an open-source data pipeline project will give you the opportunity to work on a real-world project and learn from other developers.
Browse courses on
Data Pipelines
Show steps
Show steps
Show steps
- Find an open-source data pipeline project that you are interested in.
- Identify an area where you can contribute to the project.
- Submit a pull request with your contribution.
Career center
Learners who complete Data Pipelines with TensorFlow Data Services will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
A Data Analyst is responsible for collecting, cleaning, and analyzing data to identify trends and patterns. This course can help you develop the skills necessary to perform these tasks effectively. You will learn how to use TensorFlow Data Services to streamline ETL tasks, load different datasets and custom feature vectors, and create and use pre-built pipelines for generating highly reproducible I/O pipelines for any dataset. This knowledge will help you to build a strong foundation for a career as a Data Analyst.
Data Scientist
Data Scientist
A Data Scientist is responsible for using data to solve business problems. This course can help you develop the skills necessary to perform these tasks effectively. You will learn how to use TensorFlow Data Services to streamline ETL tasks, load different datasets and custom feature vectors, and create and use pre-built pipelines for generating highly reproducible I/O pipelines for any dataset. This knowledge will help you to build a strong foundation for a career as a Data Scientist.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer is responsible for developing and deploying machine learning models. This course can help you develop the skills necessary to perform these tasks effectively. You will learn how to use TensorFlow Data Services to streamline ETL tasks, load different datasets and custom feature vectors, and create and use pre-built pipelines for generating highly reproducible I/O pipelines for any dataset. This knowledge will help you to build a strong foundation for a career as a Machine Learning Engineer.
Data Engineer
Data Engineer
A Data Engineer is responsible for designing, building, and maintaining data pipelines. This course can help you develop the skills necessary to perform these tasks effectively. You will learn how to use TensorFlow Data Services to streamline ETL tasks, load different datasets and custom feature vectors, and create and use pre-built pipelines for generating highly reproducible I/O pipelines for any dataset. This knowledge will help you to build a strong foundation for a career as a Data Engineer.
Research Scientist
Research Scientist
A Research Scientist is responsible for conducting research to develop new products and services. This course can help you develop the skills necessary to perform these tasks effectively. You will learn how to use TensorFlow Data Services to streamline ETL tasks, load different datasets and custom feature vectors, and create and use pre-built pipelines for generating highly reproducible I/O pipelines for any dataset. This knowledge will help you to build a strong foundation for a career as a Research Scientist.
Software Engineer
Software Engineer
A Software Engineer is responsible for designing, developing, and maintaining software applications. This course can help you develop the skills necessary to perform these tasks effectively. You will learn how to use TensorFlow Data Services to streamline ETL tasks, load different datasets and custom feature vectors, and create and use pre-built pipelines for generating highly reproducible I/O pipelines for any dataset. This knowledge will help you to build a strong foundation for a career as a Software Engineer.
Data Architect
Data Architect
A Data Architect is responsible for designing and managing the architecture of data systems. This course can help you develop the skills necessary to perform these tasks effectively. You will learn how to use TensorFlow Data Services to streamline ETL tasks, load different datasets and custom feature vectors, and create and use pre-built pipelines for generating highly reproducible I/O pipelines for any dataset. This knowledge will help you to build a strong foundation for a career as a Data Architect.
Database Administrator
Database Administrator
A Database Administrator is responsible for managing and maintaining databases. This course can help you develop the skills necessary to perform these tasks effectively. You will learn how to use TensorFlow Data Services to streamline ETL tasks, load different datasets and custom feature vectors, and create and use pre-built pipelines for generating highly reproducible I/O pipelines for any dataset. This knowledge will help you to build a strong foundation for a career as a Database Administrator.
Information Security Analyst
Information Security Analyst
An Information Security Analyst is responsible for protecting data and information systems from unauthorized access, use, disclosure, disruption, modification, or destruction. This course can help you develop the skills necessary to perform these tasks effectively. You will learn how to use TensorFlow Data Services to streamline ETL tasks, load different datasets and custom feature vectors, and create and use pre-built pipelines for generating highly reproducible I/O pipelines for any dataset. This knowledge will help you to build a strong foundation for a career as an Information Security Analyst.
Business Analyst
Business Analyst
A Business Analyst is responsible for analyzing business processes and developing solutions to improve efficiency and effectiveness. This course can help you develop the skills necessary to perform these tasks effectively. You will learn how to use TensorFlow Data Services to streamline ETL tasks, load different datasets and custom feature vectors, and create and use pre-built pipelines for generating highly reproducible I/O pipelines for any dataset. This knowledge will help you to build a strong foundation for a career as a Business Analyst.
Project Manager
Project Manager
A Project Manager is responsible for planning, executing, and closing projects. This course can help you develop the skills necessary to perform these tasks effectively. You will learn how to use TensorFlow Data Services to streamline ETL tasks, load different datasets and custom feature vectors, and create and use pre-built pipelines for generating highly reproducible I/O pipelines for any dataset. This knowledge will help you to build a strong foundation for a career as a Project Manager.
Technical Writer
Technical Writer
A Technical Writer is responsible for creating and maintaining documentation for software and hardware products. This course can help you develop the skills necessary to perform these tasks effectively. You will learn how to use TensorFlow Data Services to streamline ETL tasks, load different datasets and custom feature vectors, and create and use pre-built pipelines for generating highly reproducible I/O pipelines for any dataset. This knowledge will help you to build a strong foundation for a career as a Technical Writer.
Statistician
Statistician
A Statistician is responsible for collecting, analyzing, and interpreting data to draw conclusions. This course can help you develop the skills necessary to perform these tasks effectively. You will learn how to use TensorFlow Data Services to streamline ETL tasks, load different datasets and custom feature vectors, and create and use pre-built pipelines for generating highly reproducible I/O pipelines for any dataset. This knowledge will help you to build a strong foundation for a career as a Statistician.
Data Visualization Specialist
Data Visualization Specialist
A Data Visualization Specialist is responsible for creating visual representations of data to communicate insights and trends. This course can help you develop the skills necessary to perform these tasks effectively. You will learn how to use TensorFlow Data Services to streamline ETL tasks, load different datasets and custom feature vectors, and create and use pre-built pipelines for generating highly reproducible I/O pipelines for any dataset. This knowledge will help you to build a strong foundation for a career as a Data Visualization Specialist.
Actuary
Actuary
An Actuary is responsible for assessing and managing financial risks. This course can help you develop the skills necessary to perform these tasks effectively. You will learn how to use TensorFlow Data Services to streamline ETL tasks, load different datasets and custom feature vectors, and create and use pre-built pipelines for generating highly reproducible I/O pipelines for any dataset. This knowledge will help you to build a strong foundation for a career as an Actuary.
For more career information including salaries, visit:
OpenCourser.com/course/my74u3/data
Reading list
We've selected nine books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Data Pipelines with TensorFlow Data Services.
Offers a comprehensive overview of the architectural principles and best practices for designing and building data-intensive applications.
Explores different feature engineering techniques and their applications in machine learning, providing a practical guide for improving the performance of ML models.
Save
Provides a comprehensive overview of the Apache Spark framework and its capabilities for building data pipelines.
Provides a comprehensive overview of the Hadoop ecosystem, including data pipelines and processing frameworks such as MapReduce and Spark.
Focuses on using TensorFlow for data analytics tasks, such as data exploration, data cleaning, and feature engineering. It provides practical examples and code snippets to help you apply TensorFlow to real-world data analytics problems.
Provides a comprehensive overview of machine learning and offers practical guidance on using TensorFlow for building and training machine learning models. It covers a wide range of topics, from data preparation to model evaluation.
Provides a comprehensive guide to using Python for data analysis. It covers a wide range of topics, from data manipulation to data visualization. This book valuable resource for practitioners looking to use Python for data analysis tasks.
Provides a gentle introduction to data science. It covers a wide range of topics, from data collection to model evaluation. This book valuable resource for beginners looking to learn the basics of data science.
Save
Provides a comprehensive overview of deep learning. It covers a wide range of topics, from neural networks to deep learning architectures. This book valuable resource for practitioners looking to gain a deep understanding of deep learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/my74u3/data
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Effort
4 weeks of study, 4-5 hours/week
Level
Intermediate
Via
Coursera
Institution
DeepLearning.AI
Instructor
Laurence Moroney
Language
English
Transcripts
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آذربایجان دیلی
This course belongs to a
collection
called
TensorFlow: Data and Deployment. It's comprised of four courses:
- Device-based Models with TensorFlow Lite
- Advanced Deployment Scenarios with TensorFlow
- Data Pipelines with TensorFlow Data Services (viewing)
- Browser-based Models with TensorFlow.js
Good to know
Deepens theoretical understanding of machine learning modeling by emphasizing real-world applications
Provides practical guidance on deploying machine learning models for real-world use cases
Introduces TensorFlow Data Services, a powerful tool for managing and processing large datasets
Develops skills in creating and using pre-built pipelines for efficient I/O operations with TensorFlow
Offers opportunities to publish datasets to the TensorFlow Hub library, promoting collaboration and data sharing
Builds upon the TensorFlow in Practice Specialization, providing a solid foundation for advanced learners
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