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2 Day Microsoft Bot Framework Hackathon with Watchfinder

by Howard van Rooijen

Jonathan Gill CTO, Watchfinder Jonathan Gill is CTO at Watchfinder. Watchfinder buys watches from members of the public, returns them to as new condition, warranties them, and sells them back to the public via their website and retail stores. They have grown to 120+ staff, and turnover £70 million per year. On the 3rd of […]


Guest Blogger – Ed describes his three month paid summer internship at endjin Ed is studying a Mathematics BSc at UCL. He made contact in January because he was looking to gain some real world software development experience during his summer break, before his final year at University. During his three month internship we tried to expose him to as many different […]


Choosing the right cloud platform provider can be a daunting task. Take the big three, AWS, Azure, and Google Cloud Platform; each offer a huge number of products and services, but understanding how they enable your specific needs is not easy. Since most organisations plan to migrate existing applications it is important to understand how […]


Automating R Unit Tests With VSTS

by Jess Panni

I recently demonstrated how it was possible to automate the deployment of R models to Azure Machine Learning through VSTS. Of course, this is only part of the story; what about testing? It is important to ensure that all production code is adequately tested, and R is no different. Writing unit tests for R models is straight-forward […]


What is Azure Machine Learning? Azure Machine Learning (Azure ML) is a fully managed cloud service that enables you to easily build, deploy and share predictive analytics solutions. Azure ML allows you to create a predictive analytic experiment and then directly publish that as a web service. The web service API can be used in […]


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

As the interest in data science, predictive analytics and machine learning has grown in direct correlation to the amount of data that is now being captured by everyone from start ups to enterprise organisations, endjin are spending increasing amounts of time working with businesses who are looking for deeper and more valuable insights into their […]


We produced a booklet to coincide with our Future Decoded talk “The 100 Year Start-up: Embracing Disruption in Financial Services“, where we examine the challenges and opportunities in the Microsoft Cloud for the Financial Services Industry, covering the following topics: Security, Privacy & Data Sovereignty Data Ingestion, Transformation & Enrichment Big Compute Big Data – […]


As the interest in data science, predictive analytics and machine learning has grown in direct correlation to the amount of data that is now being captured by everyone from start ups to enterprise organisations, endjin are spending increasing amounts of time working with businesses who are looking for deeper and more valuable insights into their data. […]


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 […]