Keras and TensorFlow 2.x (2019)

TensorFlow 2.x Launch (2019): The release of TensorFlow 2.x was a major milestone in the deep learning community. TensorFlow 2.x was designed with simplicity and ease of use in mind, and Keras was adopted as its official high-level API. This marked the beginning of a tighter integration between the two libraries.
Eager Execution: One of the key features of TensorFlow 2.x was eager execution, which allowed dynamic computation graphs (Define-by-Run) rather than static graphs (Define-and-Run). This made TensorFlow and Keras more intuitive, enabling developers to write and debug models like they would in a traditional programming environment.
Model Deployment: TensorFlow 2.x extended the Keras ecosystem to support production deployment across different platforms, including:
- TensorFlow Lite for mobile and edge devices.
- TensorFlow.js for in-browser machine learning.
- TensorFlow Extended (TFX) for production pipelines and scaling in enterprise environments.
Seamless Development: With TensorFlow 2.x, Keras was tightly integrated with TensorFlow’s ecosystem, making it easy to move from prototyping to production. Researchers could still use Keras for rapid experimentation, while developers could leverage TensorFlow’s performance optimizations and deployment tools.

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