An introduction to MLOps with TensorFlow Extended (TFX)
HTML-код
- Опубликовано: 18 сен 2024
- 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
Thanks Rob for this talk that provides a good high-level introduction to TFX.
Concise explaination👏🏽
Clark Melissa Garcia Carol Johnson Larry
"Don't re-invent the wheel, use TFX."
Moore Karen Davis Eric Thompson Kimberly
Lewis Michelle Jones Carol Thompson Sarah
Johnson Lisa Thomas Lisa Williams Jessica
7422 Yvette Lodge
TFX is a horrible project. Yet more terrible google software that is now stale and useless