TensorFlow

How it works

TensorFlow provides tools to prepare data, describe models, and train them from examples. Evaluation compares predictions against known outcomes before a model is used by an application. Its surrounding ecosystem supports different deployment settings, so teams can consider how a trained model will receive inputs and deliver results in practice.

What you can build

Build image classifiers, forecasting tools, recommendation systems, and applications that analyze text, audio, or other data.

• Forecasting workflow

  Train a model with historical observations, compare predictions against held-out periods, and provide estimated trends to a planning dashboard for people to review.

• Image understanding feature

  Develop a classifier that recognizes defined image categories, then connect its output to a user interface that supports review and correction where appropriate.

Things to consider

A model needs representative training data and a clear evaluation target. Deployment choices should reflect response time, available hardware, and how predictions will be monitored.