Deep Learning Inference with Azure ML Studio

In the Deep Learning Inference with Azure ML Studio course, you will utilize the Multiclass Neural Network module in Azure Machine Learning Studio to train a neural network for recognizing handwritten digits. With a focus on practical application, you will deploy the trained neural network model as an Azure Web service. This project-based course leverages the popular MNIST dataset, comprising 70,000 grayscale images of hand-written digits.

Throughout the course, you will be guided to create and deploy a predictive web service, and build a Python web app to query the Azure web service API for deep learning inference. The practical emphasis of the course enables you to gain hands-on experience in utilizing Azure Machine Learning Studio as a drag-and-drop tool for rapidly building and deploying machine learning models on Azure.

• Train and evaluate a multiclass neural network on Azure ML Studio to recognize handwritten digits.
• Create and deploy a predictive web service.
• Build a Python web app to query the Azure web service API for deep learning inference.

As part of the learning experience, you will have access to a cloud desktop with pre-installed Python, Jupyter, and scikit-learn, allowing you to seamlessly engage in the hands-on project platform provided by Coursera's Rhyme. This course is designed to equip learners with practical skills for building machine learning applications using Azure Machine Learning Studio, with a focus on leveraging the capabilities of Azure Web Services to interface with machine learning workflows.