TensorFlow Extended (TFX)

Course Overview

What does this course tell?
(Please note that the following overview content is from Alison)

In this course you will learn about TensorFlow Extended (TFX) You will learn about ML engineering for production ML deployments with TFX how TFX pipelines work why we need metadata distributed processing and components model understanding and business reality production ML pipelines with TensorFlow Keynote machine learning fairness taking machine learning from research to production data validation for machine learning the production machine learning journey Machine Learning Engineering for Production MLOps and much more


We consider the value of this course from multiple aspects, and finally summarize it for you from three aspects: personal skills, career development, and further study:
(Kindly be aware that our content is optimized by AI tools while also undergoing moderation carefully from our editorial staff.)
What skills and knowledge will you acquire during this course?
In this course, students will acquire skills and knowledge related to TensorFlow Extended (TFX). They will learn about ML engineering for production ML deployments with TFX, how TFX pipelines work, why metadata, distributed processing and components are needed, model understanding and business reality, production ML pipelines with TensorFlow Keynote, machine learning fairness, taking machine learning from research to production, data validation for machine learning, the production machine learning journey, Machine Learning Engineering for Production MLOps, and much more.
lHow does this course contribute to professional growth?
This course provides professional growth by teaching students about TensorFlow Extended (TFX). Students will gain an understanding of ML engineering for production ML deployments, how TFX pipelines work, why metadata distributed processing and components are necessary, model understanding and business reality, production ML pipelines with TensorFlow Keynote, machine learning fairness, taking machine learning from research to production, data validation for machine learning, and the production machine learning journey. Additionally, students will learn about MLOps and other related topics. This course provides a comprehensive overview of TFX and its applications, giving students the knowledge and skills necessary to apply it in their professional lives.

Is this course suitable for preparing further education?
Yes, this course is suitable for preparing further education as it covers topics such as ML engineering for production ML deployments with TFX, why we need metadata distributed processing and components, model understanding and business reality production ML pipelines with TensorFlow, Keynote machine learning fairness, taking machine learning from research to production, data validation for machine learning, the production machine learning journey, Machine Learning Engineering for Production MLOps, and much more.