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 → https://goo.gle/3KejoUZ
TFX-Addons → https://goo.gle/3x6IOju
Become a Machine Learning expert → https://goo.gle/mlops-courses

Speaker: Robert Crowe

Watch more:
All Google I/O 2022 Sessions → https://goo.gle/IO22_AllSessions
ML/AI at I/O 2022 playlist → https://goo.gle/IO22_ML-AI
All Google I/O 2022 technical sessions → https://goo.gle/IO22_Sessions

Subscribe to TensorFlow → https://goo.gle/TensorFlow

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