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Automated R Deployments in Azure

by Jess Panni

It’s been great to see Microsoft embracing the R language on Azure, being able to easily operationalize R assets is changing the way organisations think about their analytical workloads. While it is trivial to publish an R model as a web service in Azure Machine Learning, there is still no easy way to integrate this […]


Machine Learning – the process is the science

by James Broome

What do machine learning and data science actually mean? This post digs into the detail behind the endjin approach to structured experimentation, arguing that the “science” is really all about following the process, allowing you to iterate to insights quickly when there are no guarantees of success.


We have produced an insightful booklet called “Embracing Disruption – Financial Services and the Microsoft Cloud” which examines the challenges and opportunities for the Financial Service Industry in the UK, through the lens of Microsoft Azure, Security, Privacy & Data Sovereignty, Data Ingestion, Transformation & Enrichment, Big Compute, Big Data, Insights & Visualisation, Infrastructure, Ops & Support, and the API Economy.


This post looks at what machine learning really is (and isn’t), dispelling some of the myths and hype that have emerged as the interest in data science, predictive analytics and machine learning has grown. Without any hard guarantees of success, it argues that machine learning as a discipline is simply trial and error at scale – proving or disproving statistical scenarios through structured experimentation.


Developing U-SQL: Local Data Folder

by Jess Panni

Those of you dabbling around with/in Azure Data Lake and Visual Studio will be aware that it is possible to run U-SQL scripts locally on your development machine. This is useful when developing and debugging scripts against small sets of data since you do not incur the overhead of submitting and running jobs up on Azure. U-SQL can access […]


A short while ago, I was trying to classify some data using Azure Machine Learning, but the training data was very imbalanced. In the attempt to build a useful model from this data, I came across the Synthetic Minority Oversampling Technique (SMOTE), an approach to dealing with imbalanced training data. This blog describes what I […]


Azure data services part 3: Azure Machine Learning

by Alice Waddicor

In parts 1 and 2 of this mini-series, I wrote brief intros to HDInsight and Stream Insight, Azure’s offerings for big data analytics and real-time analytics. Next up, Azure Machine Learning. What it’s for: Azure Machine learning provides a GUI with drag and drop pre-built components for carrying out predictive analytics. You can also plug […]


Azure data services part 2: Stream Insight

by Alice Waddicor

This blog is part of a series where I’m writing up my notes from a training session on Azure’s data services. The previous post dealt with Azure’s Hadoop implementation, HDInsight. This week, I’m going to write about Stream Insight. What it’s for: Stream Insight is an Azure service for real-time event processing. Use cases for […]


Azure data services part 1: HDInsight

by Alice Waddicor

Last Autumn, Richard Kerslake and I were lucky enough to land in the warmth of Barcelona, for a Microsoft Analytics training event. The sessions gave an introduction to Azure’s HDInsight, Stream Analytics and Machine Learning services. I’m going to write up a quick summary of what I learned about each service, starting with HDInsight. What […]


With Power BI now in public preview everywhere, you don’t need to be a “data scientist” to do data science! Power BI is a powerful tool for visualising performance, user interactions and other data for your applications. There is so much useful data sitting passively in various storage accounts, hiding interesting trends or unwanted behaviour. […]