Build ML Pipeline with TensorFlow Extended

TensorFlow Extended (TFX) is an open-source platform for building end-to-end machine learning pipelines. It consists of a series of interconnected components to help you manage data, train models, evaluate model performance, and deploy models. Here are the general steps for building an end-to-end machine learning pipeline using TensorFlow Extended.

1. Data collection and preparation: You need to gather and prepare the data to train and evaluate the model. TFX offers some data preprocessing components, such as ExampleGen and Transform, for extracting and transforming data from different sources like CSV files, databases, BigQuery, etc.
2. Feature Engineering: Before training the model, you may need to perform feature engineering on the data. TFX provides the Transform component for executing feature engineering operations such as feature scaling, one-hot encoding, and feature crossing.
3. Model training: Train machine learning models using the Trainer component. You have the option to use deep learning frameworks like TensorFlow for training.
4. Model evaluation: Evaluate the trained model using the Evaluator component. The Evaluator component compares the performance of the model on the validation dataset with the performance of previous versions.
5. Exporting and deploying the model: Finally, the trained model is exported to a model server or file system using the Pusher component for deployment and use.

By linking these components together, you can build an end-to-end machine learning pipeline to enhance the efficiency and reproducibility of machine learning workflows through automation and standardization. TFX offers a wealth of documentation and sample code to assist you in getting started with constructing your own end-to-end machine learning pipeline.