TensorFlow 2.0 on Azure: Fine-tuning BERT for question tagging

In this blog, we aim to highlight some of the ways that Azure can streamline the building, training, and deployment of your TensorFlow model. In addition to reading this blog, check out the demo discussed in more detail below, showing how you can use TensorFlow 2.0 in Azure to fine-tune a BERT (Bidirectional Encoder Representations from Transformers) model for automatically tagging questions. TensorFlow 1.x is a powerful framework that enables practitioners to build and run deep learning models at massive scale. TensorFlow 2.0 builds on the capabilities of TensorFlow 1.x by integrating more tightly with Keras (a library for building neural networks), enabling eager mode by default, and implementing a streamlined API surface. We've integrated Tensorflow 2.0 with the Azure Machine Learning service to make bringing your TensorFlow workloads into Azure as seamless as possible.