Category Archives : #AzureML

15

Aug

Conversational Retail Bots, E-Commerce Fraud Protection & More… Thanks to the Power of Microsoft AI

A peek at selective new customers benefiting from Microsoft AI and Machine Learning.

Cami Boosts Customer Engagement at Dixons Carphone

Based in the United Kingdom, Dixons Carphone is a major electronics retailer employing over 42,000 people in 11 countries, and providing consumers with products and services that help them lead seamlessly connected lives at home, in the office, and on the move. Like most retailers, Dixons Carphone has had to adapt its business to changing consumer buying patterns, including an increase in online product research and shopping. For instance, 90 percent of their customers start their shopping journey online, and a full 65 percent use their phones to assist them while shopping in-store. This resulted in a comprehensive review of their existing customer experience strategies.

Dixons Carphone partnered with Microsoft to research ways to transform their business through better customer engagement. They also wanted to and provide their store colleagues with tools to optimize the time spent with customers. They determined that artificial intelligence could be the key to offering customers a differentiated and personalized service. Specifically, Dixons Carphone investigated the capabilities of the Microsoft Bot Framework and Microsoft Cognitive Services in the context of customer interactions. The Bot

09

Aug

How Indiana Farm Bureau Insurance Uses AI to Predict Customer Churn

This post is authored by Zoran Dzunic, Data Scientist, Chris Hoder, Program Manager, and Tao Wu, Principal Data Scientist Manager, at Microsoft.

Indiana Farm Bureau Insurance (IFBI) has served Hoosiers for more than 80 years. Organized in 1934 by Indiana Farm Bureau, Inc., the company has grown to include insurance products for auto, home, life, business and farm, along with other financial services. With a home office in downtown Indianapolis and local offices in all 92 counties, Indiana Farm Bureau Insurance serves its customers with more than 400 agents and approximately 1,200 employees living and working throughout the state.

Managing customer churn is a key part of the IFBI engagement strategy. Working closely with a Microsoft data science team, IFBI analysts used the machine learning and data wrangling capabilities of Microsoft’s AI platform to identify “at risk” customers with high precision, helping them develop targeted programs for churn prevention.

The effective use of data specific to the insurance domain turns out to be a critical success factor for insurers looking to improve the accuracy of churn prediction. Existing churn prediction models from other domains often require detailed, high frequency user behavioral data. While such data is often easy to collect in

08

Aug

Bringing AI to BI – Text Analytics in Azure Machine Learning
Bringing AI to BI – Text Analytics in Azure Machine Learning

This post is authored by Raghunathan Sudarshan, Principal Software Engineering Manager, Darren Edge, UX Architect, and Jonathan Larson, Principal Data Architect, at Microsoft.

Azure Machine Learning Studio provides a Swiss-army knife of tools to operate on text datasets in a robust and efficient manner. For instance, there is a suite of built-in modules for lower-level tasks such as language detection and text pre-processing for common cleaning steps such as case normalization, stop word removal, stemming and lemmatization. Building on top of these is a collection of modules for converting pre-processed text into N-gram and skip-gram numerical features via hashing or metrics such as TF-IDF. Once a set of numerical features has been constructed, you can use any of the existing suite of learning algorithms in Azure ML to build classification, regression, recommendation or clustering models as necessary.

Besides training models using N-gram features, you can also use a set of powerful modules for tasks such as entity and key-phrase extraction that are backed by robust pre-trained models and, in turn, use them to build different kinds of features.

Azure ML leverages the powerful Vowpal Wabbit library (VW) for many of its text analytics capabilities. For example, VW is used in the

31

Jul

Find images with images
Find images with images

This post is authored by Remko de Lange, Data Solution Architect, Microsoft.

How often does it happen that you have a clear picture in mind of what you want to purchase next, but you don’t know how or where to get it? We usually start with consulting our favorite search engine on the web. However, putting your mental picture in exact words can be difficult. For example, I saw a great camping stove last time I was at my favorite camp site, not knowing that such a thing can have unpronounceable abbreviations as a name. I took a picture of the burner, though. Now, we are on the right track, as new technology can help us out.

Wouldn’t it be great if you could search for items with your image as input? Indeed, the technology presented here makes exactly that possible. While the technology names sound as exotic as the camping stove, the use of them is simple. So, first the technologies: with Microsoft R Server (version 9.1.0) comes the R MicrosoftML package that includes the machine learning transform named FeaturizeImage. It is the image featurization feature that does most of the work, as it uses a deep neural net

29

Mar

Microsoft Makes Big Data and Analytics Easier in the Cloud

This post is by Joseph Sirosh, Corporate Vice President of the Data Group at Microsoft.

This week I’m joining thousands of people attending Strata + Hadoop World in San Jose to explore the technology and business of big data and data science. As part of our participation in the conference, we are announcing several important investments to continue delivering on our commitment to make big data processing and analytics simpler and more accessible:

Advanced analytics at scale with R Server for HDInsight and the latest version of Spark for HDInsight are now available in preview: Customers can leverage their existing R skills and reuse current code to run at scale. R Server for HDInsight offers popular scalable R algorithms and the ability to parallelize any existing R function. We are also releasing the latest version of Spark for HDInsight, which can deliver 7x performance over MapReduce for most analytics. These capabilities give our customers the ability to train and run advanced analytics and ML models on larger datasets, and much faster than previously possible in the cloud. Out-of-the-box application integration, providing easier access to popular big data apps: Customers can now discover and deploy popular big data applications with HDInsight…

29

Mar

Microsoft Makes Big Data and Analytics Easier in the Cloud

This post is by Joseph Sirosh, Corporate Vice President of the Data Group at Microsoft.

This week I’m joining thousands of people attending Strata + Hadoop World in San Jose to explore the technology and business of big data and data science. As part of our participation in the conference, we are announcing several important investments to continue delivering on our commitment to make big data processing and analytics simpler and more accessible:

Advanced analytics at scale with R Server for HDInsight and the latest version of Spark for HDInsight are now available in preview: Customers can leverage their existing R skills and reuse current code to run at scale. R Server for HDInsight offers popular scalable R algorithms and the ability to parallelize any existing R function. We are also releasing the latest version of Spark for HDInsight, which can deliver 7x performance over MapReduce for most analytics. These capabilities give our customers the ability to train and run advanced analytics and ML models on larger datasets, and much faster than previously possible in the cloud. Out-of-the-box application integration, providing easier access to popular big data apps: Customers can now discover and deploy popular big data applications with HDInsight…

29

Mar

Microsoft Makes Big Data and Analytics Easier in the Cloud

This post is by Joseph Sirosh, Corporate Vice President of the Data Group at Microsoft.

This week I’m joining thousands of people attending Strata + Hadoop World in San Jose to explore the technology and business of big data and data science. As part of our participation in the conference, we are announcing several important investments to continue delivering on our commitment to make big data processing and analytics simpler and more accessible:

Advanced analytics at scale with R Server for HDInsight and the latest version of Spark for HDInsight are now available in preview: Customers can leverage their existing R skills and reuse current code to run at scale. R Server for HDInsight offers popular scalable R algorithms and the ability to parallelize any existing R function. We are also releasing the latest version of Spark for HDInsight, which can deliver 7x performance over MapReduce for most analytics. These capabilities give our customers the ability to train and run advanced analytics and ML models on larger datasets, and much faster than previously possible in the cloud. Out-of-the-box application integration, providing easier access to popular big data apps: Customers can now discover and deploy popular big data applications with HDInsight…

21

Mar

Hadoop is famously scalable. Cloud computing is famously scalable. But R – the preferred software and lingua franca of data scientists worldwide – not so much. But what if we seamlessly combined Hadoop with the cloud and R to create a scalable data science platform? Imagine exploring, transforming, modeling, and scoring data at any scale from the comfort of your favorite R environment. Now, imagine calling a simple R function to operationalize your predictive model as a scalable, cloud-based web service. 

Learn how to leverage the magic of Hadoop on-premises or in the cloud to run your R code, with thousands of open source R extension packages, and distributed implementations of the most popular machine learning algorithms, at scale. Click here or on the image below to register for this free webinar.

ML Blog Team

21

Mar

Hadoop is famously scalable. Cloud computing is famously scalable. But R – the preferred software and lingua franca of data scientists worldwide – not so much. But what if we seamlessly combined Hadoop with the cloud and R to create a scalable data science platform? Imagine exploring, transforming, modeling, and scoring data at any scale from the comfort of your favorite R environment. Now, imagine calling a simple R function to operationalize your predictive model as a scalable, cloud-based web service. 

Learn how to leverage the magic of Hadoop on-premises or in the cloud to run your R code, with thousands of open source R extension packages, and distributed implementations of the most popular machine learning algorithms, at scale. Click here or on the image below to register for this free webinar.

ML Blog Team

21

Mar

Hadoop is famously scalable. Cloud computing is famously scalable. But R – the preferred software and lingua franca of data scientists worldwide – not so much. But what if we seamlessly combined Hadoop with the cloud and R to create a scalable data science platform? Imagine exploring, transforming, modeling, and scoring data at any scale from the comfort of your favorite R environment. Now, imagine calling a simple R function to operationalize your predictive model as a scalable, cloud-based web service. 

Learn how to leverage the magic of Hadoop on-premises or in the cloud to run your R code, with thousands of open source R extension packages, and distributed implementations of the most popular machine learning algorithms, at scale. Click here or on the image below to register for this free webinar.

ML Blog Team