TensorFlow Extended (TFX)

Right now, my. focus is on Machine Learning Pipelines, Platforms and similar topics.

Google TFX is one such tool. It helps you build a complete ML pipeline.

A TFX pipeline is a sequence of components that implement an ML pipeline for scalable, high-performance machine learning tasks, including modeling, training, serving inference, and managing deployments to online, native mobile, and JavaScript targets.

The pipeline components are built using TFX libraries which can also be used individually.

TFX Libraries

TensorFlow Data Validation

To understand, validate, and monitor ML data at scale.

TensorFlow Transform

To preprocess data into a suitable format, including converting between formats, tokenizing and stemming text and forming vocabularies, and performing a variety of numerical operations such as normalization.

TensorFlow Model Analysis

To compute and visualize evaluation metrics for models by evaluating model performance to ensure that it meets specific quality thresholds and behaves as expected for all relevant slices of data.

TensorFlow Serving

To support model versioning (for model updates with a rollback option) and multiple models (for experimentation via A/B testing), while ensuring that concurrent models achieve high throughput on hardware accelerators (GPUs and TPUs) with low latency.

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