Five years ago, Google open-sourced TensorFlow for research and production.
“Our goal was to expand access to state-of-the-art machine learning tools so anyone could use them,” the company said in a statement on Friday.
Since then, thousands of people outside of Google have contributed code, created educational content and organised developer events around the world to support TensorFlow and the growing machine learning community.
TensorFlow supports multiple programming languages and environments.
You can train your own model (with no coding required) using the Teachable Machine.
For instance, you could teach a model to recognize images, or sounds that you record using your microphone.
TensorFlow includes a powerful Python library.
TensorFlow Lite enables you to build machine learning-powered apps on mobile and small embedded devices.
“A group of engineering students in India used TensorFlow Lite to develop an Android app that provides local air quality information using a smartphone camera,” Google said.
TensorFlow Lite Micro lets you run machine learning models on microcontrollers (tiny computers that can fit in the palm of your hand).
TensorFlow also includes a large set of tools and best practices for Responsible AI, including the What-If Tool which tests how machine learning models will work for different people in hypothetical situations.
“TensorFlow includes a complete set of tools to power production ML systems, and even supports the latest research in Quantum computing,” Google said.