TensorFlow Extended (TFX) is an end-to-end platform for deploying production ML pipelines

TensorFlow Data Validation (TFDV) helps developers understand, validate, and monitor their ML data at scale. TFDV is used to analyze and validate petabytes of data at Google every day, and has a proven track record in helping TFX users maintain the health of their ML pipelines.

When applying machine learning to real world datasets, a lot of effort is required to preprocess data into a suitable format. This includes converting between formats, tokenizing and stemming text and forming vocabularies, and performing a variety of numerical operations such as normalization. You can do it all with tf.Transform.

TensorFlow Model Analysis (TFMA) enables developers to compute and visualize evaluation metrics for their models. Before deploying any machine learning (ML) model, ML developers need to evaluate model performance to ensure that it meets specific quality thresholds and behaves as expected for all relevant slices of data. For example, a model may have an acceptable AUC over the entire eval dataset, but underperform on specific slices. TFMA gives developers the tools to create a deep understanding of their model performance.

Machine Learning (ML) serving systems need 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. TensorFlow Serving has proven performance handling tens of millions of inferences per second at Google.