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The theme of this year’s British Science Week (6 – 15 March 2020) is “Our Diverse Planet”. We’ll be getting involved by speaking to school children about the work we’ve been doing with Oxfordshire-based OceanMind (part of the Microsoft AI for Good programme) to help them combat illegal fishing, hopefully inspiring some of the next generation of data scientists!


NDC London 2020 – My highlights

by Ed Freeman

Ed attended NDC London 2020, along with many of his endjin colleagues. In this post he summarises and reflects upon his favourite sessions of the conference including; “OWASP Top Ten proactive controls” by Jim Manico, “There’s an Impostor in this room!” by Angharad Edwards, “How to code music?” by Laura Silvanavičiūtė, “ML and the IoT: Living on the Edge” by Brandon Satrom, “Common API Security Pitfalls” by Philippe De Ryck, and “Combatting illegal fishing with Machine Learning and Azure – for less than £10 / month” by Jess Panni & Carmel Eve.


Along with several of my endjin colleagues, I attended NDC London in January this year – here’s a run through of the sessions I attended on Day 2 and my thoughts. This day was UI heavy, with sessions on Vuejs and UI testing, but I also learned more about GraphQL and writing high performance C# code.


A retrospective of my first day at NDC London 2020, taking in sessions on AI and Machine Learning, Capability Mapping, Micro Frontends, Diagnostics and Blazor.


Using Python inside SQL Server

by Ed Freeman

Do you have a bunch of data in SQL Server that you’re using ODBC/JDBC to pull data to work with in Python? Using SQL Server’s Python integration, you can connect to a SQL Server instance within your preferred IDE and perform the computations on the SQL Server Machine. No more clunky data transferring. Operationalizing a Python model/script is as easy as calling a stored procedure. Any application that can speak to SQL Server can invoke the Python code and retrieve the results. Easy! This blog will provide a few, simple examples which make use of this capability to carry out some simple Python commands, so you can get up and running as quickly as possible.


Welcome to an internship at endjin!

by Ed Freeman

A career in software engineering doesn’t need to start with a Computer Science degree. The underlying traits of problem solving, a willingness to learn and the ability to collaborate well can be built in any field. Internships provide a great way to get your foot-in-the-door in the professional world, and arm you with some real-life experience for future endeavours. This post describes an internship at endjin, including the type of work you could be asked to do and what you could learn.


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.


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 […]