TensorFlow Extended
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TensorFlow Extended (TFX) is a set of tools that help you build, train, and manage machine learning (ML) pipelines. TFX is a popular tool used in the industry and is helpful for automating the end-to-end (E2E) workflow associated with ML pipelines and life cycles of models. With TFX, you can use TensorFlow – a leading ML library – to train your model and then deploy it to the cloud for production.
What is a Machine Learning Pipeline?
A machine learning pipeline is the complete process of building, training, and deploying a machine learning model. The pipeline includes the following steps:
- Data Collection & Preparation: Gathering and preparing raw data for modeling.
- Feature Engineering: Creating new features from the raw data that are better suited for modeling.
- Model Training: Training a machine learning model on the prepared data.
- Model Evaluation: Evaluating the performance of the trained model.
- Model Deployment: Deploying the trained model to production.
TensorFlow Extended Supports Model Development
TensorFlow Extended (TFX) is a popular open-source ML framework that helps you build, train, and deploy ML pipelines. TFX provides a set of tools and components that make it easy to create E2E ML pipelines. TFX is designed to be flexible and scalable, so you can use it to build pipelines for different types of ML tasks.
For example, you can use TFX to build pipelines for image classification, natural language processing, and time series forecasting.
Value and Benefits of TFX
TFX offers many benefits for ML pipeline development, including:
