Running TensorFlow 2.2 in Azure Machine Learning Studio

One challenge with keeping up with bleeding edge AI and ML frameworks is that the changes can easily outpace APIs and SDKs build on top of these tools. Fortunately, since these tools are largely open-source and made available through industry common package managers, these updates can be used before the frameworks support them, albeit with some workarounds. Azure Machine Learning Studio (AzureML) is a platform service that provides full end to end management of Machine Learning and Data Science workloads. The tool provides experiment tracking, data set management, model repository, and deployment services to enable data scientists to train/validate/deploy their models in a cloud scale environment. To run TensorFlow 2.2 in AzureML is really pretty trivial, given the existing support for the TensorFlow framework.