15 TFX: MLOps and deploying models with TensorFlow
This chapter covers
- Writing an end-to-end data pipeline using TFX (TensorFlow-Extended)
- Training a simple neural network through the TFX Trainer API
- Using Docker to containerize model serving (inference) and present it as a service
- Deploying the model on your local machine so it can be used through an API
In chapter 14, we looked at a very versatile tool that comes with TensorFlow: the TensorBoard. TensorBoard is a visualization tool that helps you understand data and models better. Among other things, it facilitates
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Monitoring and tracking model performance
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Visualizing data inputs to models (e.g., images, audio)
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Profiling models to understand their performance or memory bottlenecks
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