# Topics > Artificial neural networks > Machine learning, deep learning >  Azure Machine Learning, Microsoft Corporation, Redmond, Washington, USA

## Airicist

Developer - Microsoft Corporation

Home page - azure.com/ml

Microsoft Azure on Wikipedia

CTO -  Mark Russinovich

----------


## Airicist

Overview of Azure Machine Learning Marketplace

Published on Oct 6, 2015




> The vision of the Azure Machine Learning team is to make data science accessible to everyone, including those that do not have a data science background. The Azure Machine Learning Marketplace provides turn-key ready, end-to-end solutions. In this webinar, we will discuss some of these end-to-end applications. We will focus on Recommendations, Customer Churn Prediction, and Text Analytics with demos on how to get started with each.
> 
> "Introducing Text Analytics in the Azure ML Marketplace"
> 
> by Nagender Parimi
> April 8, 2015

----------


## Airicist

Azure Machine Learning – An Overview of New Capabilities

Published on Oct 6, 2015




> Azure Machine Learning (ML) has reached the GA milestone. Azure ML offers a streamlined experience for data scientists of all skill levels, from getting started with nothing but a browser window, to using drag and drop gestures and simple data flow graphs to set up experiments, to operationalizing an ML model into web services within seconds. ML Studio features a library of time-saving sample experiments, R and Python packages and best-in-class algorithms from Microsoft businesses like Xbox and Bing.
> 
> Learn more here:
> Machine Learning documentation

----------


## Airicist

Overview of Azure Machine Learning

Published on Oct 6, 2015




> This provides an overview of the Azure Machine Learning Service. A browser based workbench for the data science workflow, which includes authoring, evaluating and publishing predictive models.

----------


## Airicist

Cloud Machine Learning for Engineers

Published on Oct 6, 2015




> In this session Dan Grecoe explains Azure Machine Learning through a comprehensive end-to-end example that he builds during the session and that encompasses:
> •Problem detection
> •Algorithm selection
> •Machine learning model creation and deployment as a RESTful web service
> •Consumption of the machine learning model
> 
> The session is intended for engineers, and Dan himself is an engineer, so he does not delve into a deep understanding of complex mathematical models behind machine learning, but instead focuses on the concepts of machine learning to demystify cloud machine learning.
> 
> Azure Machine Learning for Engineers

----------


## Airicist

Azure Machine Learning

Published on Oct 7, 2015




> An introduction to Azure Machine Learning. Making machine learning accessible to all.

----------


## Airicist

Customer Churn Prediction by Azure Machine Learning

Published on Oct 7, 2015




> See what the Customer Churn Prediction service by Azure Machine Learning can do for your business. Get started by visiting our Marketplace Offer.

----------


## Airicist

Predicting Customer Churn using Mobile Telco Data

Published on Oct 7, 2015




> This video discusses a use case of how a mobile telco provider uses machine learning to predict which customers are likely to leave. It includes a demo and a general overview of how multiple services can be composed together.

----------


## Airicist

Article "Microsoft Azure CTO: Machine Learning Can Be Killer Cloud App"

by Michael Moore
October 9, 2015

----------


## Airicist

Azure's open source journey for cloud innovation

Published on Mar 8, 2016




> Join Mark Russinovich, Microsoft Azure CTO, to learn what organizations around the globe are doing with open source technologies in Azure. From adding value to their existing datacenter investments, to exploring new paradigms to transform their businesses, Azure provides an open and flexible platform that empowers them to succeed. Mark also discusses the value of our technology partnerships with companies like Docker, Red Hat, Canonical, Mesosphere and others, and how they are helping Azure provide a hyper-scale platform for our customers’ open source needs.


Build open source apps at hyper scale with hyper speed

Open source software on Azure

----------

