Azure Machine Learning services (preview) is an integrated, end-to-end data science and advanced analytics solution. It helps professional data scientists to prepare data, develop experiments, and deploy models at cloud scale. This Quickstart shows you how to create experimentation and model management accounts in Azure Machine Learning Preview. It also shows you how to install the Azure Machine Learning Workbench desktop application and CLI tools. Next, you take a quick tour of Azure Machine Learning Preview features by using the Iris flower dataset to build a model that predicts the type of iris based on some of its physical characteristics.

In the last two columns, I explored the features and services provided by Azure Machine Learning Studio. In September 2017, Microsoft announced a new suite of tools for doing machine learning (ML) on Azure. The cornerstone of these new tools is Azure Machine Learning Workbench. However, what could be better for doing ML than the simple drag-and-drop interface of Machine Learning Studio? Machine Learning Studio is an ideal tool for creating ML models without having to write code, but it falls short in several areas. First and foremost, the tool's simplicity requires a "black box" approach.

Azure Machine Learning is an integrated, end-to-end data science and advanced analytics solution. It enables data scientists to prepare data, develop experiments, and deploy models at cloud scale. Together, these applications and services help significantly accelerate your data science project development and deployment. Azure Machine Learning fully supports open source technologies. You can execute your experiments in managed environments such as Docker containers and Spark clusters.

Microsoft, just like many of its competitors, has gone all in on machine learning. That emphasis is on full display at the company's Ignite conference this where, where the company today announced a number of new tools for developers who want to build new A.I. models and users who simply want to make use of these pre-existing models -- either from their own teams or from Microsoft. For developers, the company launched three major new tools today: the Azure Machine Learning Experimentation service, the Azure Machine Learning Workbench and the Azure Machine Learning Model Management service. In addition, Microsoft also launched a new set of tools for developers who want to use its Visual Studio Code IDE for building models with CNTK, TensorFlow, Theano, Keras and Caffe2. And for non-developers, Microsoft is also bringing Azure-based machine learning models to Excel users, who will now be able to call up the AI functions that their company's data scientists have created right from their spreadsheets.