An introduction to MLOps with TensorFlow Extended (TFX)

Deploying advanced machine learning technology to serve customers and/or business needs requires a rigorous approach and production-ready systems. An ML application in production requires modern software development methodology, as well as issues unique to ML and data science. Hear about the importance of MLOps, the use of ML pipeline architectures for implementing production ML applications, rigorous analysis of model performance and sensitivity, and review Google’s experience with TensorFlow Extended (TFX).
Resources:
TensorFlow website → goo.gle/3KejoUZ
TFX-Addons → goo.gle/3x6IOju
Become a Machine Learning expert → goo.gle/mlops-...
Speaker: Robert Crowe
Watch more:
All Google I/O 2022 Sessions → goo.gle/IO22_A...
ML/AI at I/O 2022 playlist → goo.gle/IO22_M...
All Google I/O 2022 technical sessions → goo.gle/IO22_S...
Subscribe to TensorFlow → goo.gle/Tensor...
#GoogleIO

[@jeromeeusebius]

Thanks Rob for this talk that provides a good high-level introduction to TFX.

[@armagaan009]

Concise explaination👏🏽

[@VictorSwift-c8u]

Lubowitz Loaf

[@tomiinai]

"Don't re-invent the wheel, use TFX."

[@StephanyCrenshaw]

Lewis Michelle Jones Carol Thompson Sarah

[@togo7022]

TFX is a horrible project. Yet more terrible google software that is now stale and useless

[@SusannaMarcus]

7422 Yvette Lodge