How to build an ML pipeline with TFX
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19.03.21 – 22 4885:55Популярные
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176 дней – 25542:30Опубликовано 19 марта 2021, 16:00
Tensorflow Extended (TFX) is an end-to-end platform for deploying production ML pipelines that allows developers to complete scalable, high-performing machine learning tasks. In this episode of AI Adventures, we give you a quick overview of TFX - showing you how it makes creating, managing, and deploying production ML pipelines.
Chapters:
0:00 - Intro
1:08 ML workflow steps
2:15 How to create end to end ML pipeline
3:00 How does TFX pipeline works
3:30 What is a component
4:55 Define the different components of TFX pipeline
Check out the rest of the AI Adventures playlist → goo.gl/UC5usG
Subscribe to Google Cloud Tech → goo.gle/GoogleCloudTech
Product: AIML, Tensorflow Extended; fullname: Priyanka Vergadia;
#AIAdventures
Chapters:
0:00 - Intro
1:08 ML workflow steps
2:15 How to create end to end ML pipeline
3:00 How does TFX pipeline works
3:30 What is a component
4:55 Define the different components of TFX pipeline
Check out the rest of the AI Adventures playlist → goo.gl/UC5usG
Subscribe to Google Cloud Tech → goo.gle/GoogleCloudTech
Product: AIML, Tensorflow Extended; fullname: Priyanka Vergadia;
#AIAdventures
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