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Google Cloud Training
This is a self-paced lab that takes place in the Google Cloud console. In this lab, you'll learn to create your own machine learning pipelines using TensorFlow Extended (TFX) and Apache Airflow as the orchestrator.
Register for this course and see more details by visiting:
OpenCourser.com/course/ybwy8f/orchestrating
What's inside
Syllabus
Orchestrating a TFX Pipeline with Airflow
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops expertise in the use of TFX (TensorFlow Extended) and Apache Airflow in an orchestrated pipeline for machine learning
Emphasizes hands-on practice in the Google Cloud console, providing a practical learning experience
Taught by Google Cloud Training, recognized for their expertise in cloud computing and machine learning
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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 Orchestrating a TFX Pipeline with Airflow with these
activities:
Review Machine Learning Concepts
Show steps
Solidify your understanding of fundamental machine learning concepts prior to starting the course.
Browse courses on
Machine Learning
Show steps
-
Revisit lectures and readings from previous machine learning courses.
-
Complete practice problems and exercises on platforms like LeetCode or Kaggle.
-
Review online tutorials and videos on key concepts such as linear regression, decision trees, and support vector machines.
-
Engage in discussions or forums to clarify any doubts or reinforce your understanding.
Practice TFX and Airflow Concepts
Show steps
Develop proficiency in TFX and Airflow before starting the course through hands-on practice.
Show steps
-
Set up a development environment with TFX and Airflow.
-
Create and execute simple TFX pipelines using provided templates.
-
Build and schedule Airflow DAGs to manage and monitor the pipelines.
-
Troubleshoot any errors or issues encountered during the practice exercises.
Build a Resource Collection
Show steps
Curate a comprehensive collection of resources related to the course topics to support your learning.
Browse courses on
Resources
Show steps
-
Gather relevant articles, videos, tutorials, and documentation on TFX, Airflow, and other related concepts.
-
Create a structured system for organizing and accessing the resources, such as a shared document or repository.
-
Review and update the collection regularly to ensure it remains current and valuable.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Join a Study Group
Show steps
Collaborate with fellow students by forming a study group to discuss course concepts and work on assignments together.
Show steps
-
Identify potential study partners who share similar learning goals.
-
Establish regular meeting times and create a shared workspace for collaboration.
-
Discuss course materials, solve problems together, and provide feedback to each other.
-
Leverage the collective knowledge and support of the group to enhance your understanding.
Follow Along with Guided Tutorials
Show steps
Supplement your course learning by following guided tutorials and walkthroughs.
Show steps
-
Identify reputable sources for TFX and Airflow tutorials, such as the official documentation or Coursera's Guided Projects.
-
Select tutorials that align with your learning goals and skill level.
-
Follow the instructions carefully and complete the exercises provided in the tutorials.
-
Refer back to the tutorials as needed to reinforce your understanding.
Build a Mini Machine Learning Project
Show steps
Apply your knowledge and skills by building a small-scale machine learning project that utilizes TFX and Airflow.
Browse courses on
Machine Learning Projects
Show steps
-
Define a problem statement and gather a suitable dataset.
-
Design and implement a machine learning pipeline using TFX.
-
Use Airflow to orchestrate and schedule the pipeline.
-
Evaluate the performance of your pipeline and make improvements.
Build a Machine Learning Model Using TFX and Airflow
Show steps
Demonstrate your mastery of course concepts by building a robust machine learning model using TFX and Airflow.
Browse courses on
Machine Learning Models
Show steps
-
Select a complex dataset and define a clear business problem.
-
Design and implement a comprehensive machine learning pipeline using TFX.
-
Use Airflow to orchestrate and monitor the pipeline.
-
Evaluate the performance of your model and identify areas for improvement.
Review Machine Learning Concepts
Show steps
Solidify your understanding of fundamental machine learning concepts prior to starting the course.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Revisit lectures and readings from previous machine learning courses.
- Complete practice problems and exercises on platforms like LeetCode or Kaggle.
- Review online tutorials and videos on key concepts such as linear regression, decision trees, and support vector machines.
- Engage in discussions or forums to clarify any doubts or reinforce your understanding.
Practice TFX and Airflow Concepts
Show steps
Develop proficiency in TFX and Airflow before starting the course through hands-on practice.
Show steps
Show steps
Show steps
- Set up a development environment with TFX and Airflow.
- Create and execute simple TFX pipelines using provided templates.
- Build and schedule Airflow DAGs to manage and monitor the pipelines.
- Troubleshoot any errors or issues encountered during the practice exercises.
Build a Resource Collection
Show steps
Curate a comprehensive collection of resources related to the course topics to support your learning.
Browse courses on
Resources
Show steps
Show steps
Show steps
- Gather relevant articles, videos, tutorials, and documentation on TFX, Airflow, and other related concepts.
- Create a structured system for organizing and accessing the resources, such as a shared document or repository.
- Review and update the collection regularly to ensure it remains current and valuable.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Join a Study Group
Show steps
Collaborate with fellow students by forming a study group to discuss course concepts and work on assignments together.
Show steps
Show steps
Show steps
- Identify potential study partners who share similar learning goals.
- Establish regular meeting times and create a shared workspace for collaboration.
- Discuss course materials, solve problems together, and provide feedback to each other.
- Leverage the collective knowledge and support of the group to enhance your understanding.
Follow Along with Guided Tutorials
Show steps
Supplement your course learning by following guided tutorials and walkthroughs.
Show steps
Show steps
Show steps
- Identify reputable sources for TFX and Airflow tutorials, such as the official documentation or Coursera's Guided Projects.
- Select tutorials that align with your learning goals and skill level.
- Follow the instructions carefully and complete the exercises provided in the tutorials.
- Refer back to the tutorials as needed to reinforce your understanding.
Build a Mini Machine Learning Project
Show steps
Apply your knowledge and skills by building a small-scale machine learning project that utilizes TFX and Airflow.
Browse courses on
Machine Learning Projects
Show steps
Show steps
Show steps
- Define a problem statement and gather a suitable dataset.
- Design and implement a machine learning pipeline using TFX.
- Use Airflow to orchestrate and schedule the pipeline.
- Evaluate the performance of your pipeline and make improvements.
Build a Machine Learning Model Using TFX and Airflow
Show steps
Demonstrate your mastery of course concepts by building a robust machine learning model using TFX and Airflow.
Browse courses on
Machine Learning Models
Show steps
Show steps
Show steps
- Select a complex dataset and define a clear business problem.
- Design and implement a comprehensive machine learning pipeline using TFX.
- Use Airflow to orchestrate and monitor the pipeline.
- Evaluate the performance of your model and identify areas for improvement.
Career center
Learners who complete Orchestrating a TFX Pipeline with Airflow will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers combine software engineering and machine learning expertise to design, implement, and maintain machine learning models and systems. This course can help build a foundation for the role by providing hands-on experience with TensorFlow Extended (TFX), a popular end-to-end machine learning platform.
Data Engineer
Data Engineer
A Data Engineer serves as a critical bridge between Data Science and Software Engineering, bringing together data expertise, programming skills, and software engineering best practices to maintain data pipelines and guarantee data integrity. The course, *Orchestrating A TFX Pipeline with Airflow* can help build a foundation for the role by introducing students to the use of Apache Airflow, a popular workflow management system for orchestrating data pipelines.
Data Scientist
Data Scientist
Data Scientists use scientific methods, processes, algorithms, and systems to extract knowledge and insights from data in various forms, both structured and unstructured. This course can be useful for those interested in learning how to leverage Apache Airflow and TFX to orchestrate and manage machine learning pipelines.
Software Engineer
Software Engineer
Software Engineers design, develop, test, and maintain software systems. This course can be useful for those interested in learning how to use Apache Airflow, a popular workflow management system for orchestrating data pipelines.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to identify trends and patterns, and to help businesses make informed decisions. This course may be useful for those interested in learning how to use TensorFlow Extended (TFX) to orchestrate and manage machine learning pipelines.
Project Manager
Project Manager
Project Managers plan and execute projects, ensuring that they are completed on time, within budget, and to the required quality standards. This course can be useful for those interested in learning how to use Apache Airflow, a popular workflow management system for orchestrating data pipelines.
Business Analyst
Business Analyst
Business Analysts work with stakeholders to understand their business needs and translate those needs into technical requirements. This course can be useful for those interested in learning how to use Apache Airflow, a popular workflow management system for orchestrating data pipelines.
Systems Analyst
Systems Analyst
Systems Analysts design, develop, and implement computer systems. This course can be useful for those interested in learning how to use Apache Airflow, a popular workflow management system for orchestrating data pipelines.
Database Administrator
Database Administrator
Database Administrators are responsible for the performance, security, and integrity of databases. This course can be useful for those interested in learning how to use Apache Airflow, a popular workflow management system for orchestrating data pipelines.
Web Developer
Web Developer
Web Developers design, develop, and maintain websites and web applications. This course can be useful for those interested in learning how to use Apache Airflow, a popular workflow management system for orchestrating data pipelines.
Data Architect
Data Architect
Data Architects design and build data architectures to meet the needs of an organization. This course can be useful for those interested in learning how to use Apache Airflow, a popular workflow management system for orchestrating data pipelines.
Cloud Architect
Cloud Architect
Cloud Architects design, build, and manage cloud computing systems. This course can be useful for those interested in learning how to use Apache Airflow, a popular workflow management system for orchestrating data pipelines.
DevOps Engineer
DevOps Engineer
DevOps Engineers work to bridge the gap between development and operations teams, ensuring that software is delivered quickly and efficiently. This course can be useful for those interested in learning how to use Apache Airflow, a popular workflow management system for orchestrating data pipelines.
IT Manager
IT Manager
IT Managers plan, organize, and direct the activities of an organization's IT department. This course can be useful for those interested in learning how to use Apache Airflow, a popular workflow management system for orchestrating data pipelines.
Statistician
Statistician
Statisticians collect, analyze, and interpret data to provide insights and make predictions. This course can be useful for those interested in learning how to use TensorFlow Extended (TFX) to orchestrate and manage machine learning pipelines.
For more career information including salaries, visit:
OpenCourser.com/course/ybwy8f/orchestrating
Reading list
We've selected seven 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
Orchestrating a TFX Pipeline with Airflow.
Offers a comprehensive and practical guide to building and deploying ML models with Python. It covers a wide range of ML algorithms and techniques, making it an excellent resource for gaining a solid foundation in ML.
Offers a comprehensive and accessible introduction to deep learning with Python. It covers the fundamentals of deep learning, neural networks, and their applications. This book provides a good starting point for those interested in understanding the basics of deep learning.
Offers a mathematical and statistical treatment of pattern recognition and ML. It covers topics such as Bayesian inference, dimensionality reduction, and support vector machines. This book is recommended for those interested in a more theoretical and mathematical perspective on ML.
Save
Provides a comprehensive overview of data mining techniques and algorithms. It covers topics such as data preprocessing, clustering, and classification. This book serves as a valuable reference for those interested in gaining a practical understanding of data mining.
Provides a practical guide to using R for ML. It covers a wide range of ML algorithms and explores their implementation in R. This book is recommended for those interested in applying ML techniques using R.
Provides a beginner-friendly introduction to deep learning using the fastai library and PyTorch. It offers a practical and code-centric approach to building and training deep learning models.
Offers a practical guide to data analysis using Pandas, a popular Python library for data manipulation and analysis. It covers topics such as data exploration, wrangling, and visualization.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ybwy8f/orchestrating
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Effort
90 minutes
Level
Introductory
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
English
Good to know
Develops expertise in the use of TFX (TensorFlow Extended) and Apache Airflow in an orchestrated pipeline for machine learning
Emphasizes hands-on practice in the Google Cloud console, providing a practical learning experience
Taught by Google Cloud Training, recognized for their expertise in cloud computing and machine learning
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