machine learning

Big Data Course Plymouth Uni
No small talk for big data: Software Solved spend time with Amazon scientist at Plymouth University
No small talk for big data: Software Solved spend time with Amazon scientist at Plymouth University 629 437 Aneeq Rehman

If you want to learn about how machine learning is being applied in the business world, e-commerce giant Amazon is a good place to start.

Software Solved’s Aneeq ur Rehman spent a week with big data experts including Amazon’s Dr Zhenwen Dai at a course at Plymouth University.

Supported by the Environmental Futures of Big Data Impact Lab, the course was led by Dr Antonia Rago alongside key academic figures from the Mathematical Sciences department including Dr Mu Nui, Dr Matthew Craven, Dr Malgorzata Wojtys and Dr Craig McNeile.

That’s a lot of Doctors – all experts in their respective fields!

Dr Zhenwen Dai, a Machine Learning Scientist at Amazon, whose, research interests include the development of scalable probabilistic models and inference methods for autonomous learning from real world data was one of the keynote speakers who shared his expertise with Aneeq and others on the course on probabilistic approaches to deal with huge volumes of data.

Aneeq, who is at Software Solved as part of our own machine learning research project, running in partnership with Plymouth University and funded by Innovate UK, said: “The course was about enhancing people’s understanding of big data and how to use and derive value from large volumes of data to make smarter decisions. Large volumes of data in machine learning often require flexible models that can imitate the way the human brain processes data and assimilates multiple sources. That was a key aspect of what we learnt.”

Topics covered included Gaussian process, machine learning and neural networks.

  • Dr Mu Niu – overview of Gaussian Process, different models and how they function in theory and practise
  • Dr Matthew Craven and Dr Malgorzata Wojtys – detailed explanation into various machine learning parametric methods
  • Dr Craig McNeile – summary of deep learning and neural networks and running hands-on workshops and exercises to learn more about topological data analysis and high-performance computer facilities.

Aneeq, already a Masters Graduate in Data Science himself, added: “It was a good knowledge sharing platform with a blend of workshops, practical exercises and lectures. We reviewed many interesting topics such as topological data analysis, neural network design and artificial neural networks. In addition to this, we also got insights into some of the cutting-edge research and practices in the world of big data and AI.

There were also intriguing discussions over the use of neural networks and gaussian processes and good comparisons over their use and limitations. Some of it was a good refresher session for me, some of it really new and interesting.

It’s all highly relevant to the research work I’m doing at Software Solved into the application of machine learning and advanced data analytics for risk modelling and mitigation for the insurance sector.

It was great to meet with other students and learn from the academics and other organisations. It’s technical but truly fascinating and a fundamental part of how organisations will operate, interpret and derive value from the data in the 21st century- the advantages of which are manifold.”

Author: Aneeq Ur Rehman is Software Solved’s Knowledge Transfer Partnership Associate from Plymouth University

Software Solved are specialists in giving organisation’s a competitive edge through software and data. Contact 01392 453344 to find out more.

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Innovate UK Machine Learning Project Team
Living the dream with Eric Clapton, ice-cream and machine learning…
Living the dream with Eric Clapton, ice-cream and machine learning… 1024 680 Jon Stace

So, what are the best collaborations in history? Eric Clapton lends a mighty ‘slow’ hand to The Beatles’ While My Guitar Gently Weeps. Ben Cohen and Jerry Greenfield shared a passion for the frozen stuff before going into business together. Today they are worth an estimated $150m, each. And I’m not even going to mention what became of the pairing of the ‘Steves’, as in Jobs and Wozniak.

Machine learning collaboration

At Software Solved we’ve got our very own and very exciting collaboration going on. It’s not music or food related even though we do have plenty of talent in that department. But we’ll save that for a later blog.

We’ve teamed up with some very clever people at Plymouth University and our client RSA UK. Backed by funding from Innovate UK, we have collectively fired the starting gun on an exciting two-year research programme into machine learning and advanced data analytics for the corporate insurance sector.

Data insight

Leveraging data architectures to model relationships and interactions to mitigate risk, the aim is to derive greater value from the large volumes of datasets used in the industry.

Dr Ian Howard and Dr Luciana Dalla Valle of the University of Plymouth are providing research expertise in the areas of machine learning and pattern recognition, data modelling, statistics and predictive analytics, with Aneeq Ur Rehman, Knowledge Transfer Partnership Associate, being based at Software Solved for the project.

RSA UK role

RSA UK are another key partner in the project. We already have a close working relationship with them. Their underwriters use the award-winning RSAred application for a real-time view of risk, along with a secure, mobile-accessible client risk portal for brokers and customers to understand, benchmark and manage property and casualty risk in real time, both created by Software Solved.

With all this in mind, and with RSA UK’s experience as one of the world’s longest standing and most forward-thinking of general insurers, it made perfect sense to invite them to be part of the project. They will be providing the data and working closely with us so they and their clients can benefit from the advantages of automated data integration in risk assessment.

We’ll be updating everyone on the project as it progresses and we will be looking to hold some workshops at key milestones to involve and share with others.

Special thanks to Innovate UK for funding the project and if you’d be interested in joining one of those workshops (in person or online), or if there are other truly great collaborations we clearly should have flagged, then walk this way and tell me!

Jon Stace, Principal Technical Consultant and project lead




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Machine Learning
Machine Learning 150 150 Simon Hollingworth

Two-Year Project in Machine Learning With University of Plymouth

Funded by Innovate UK, part of UK Research and Innovation, we will work with the university on a major two-year research programme into machine learning and advanced data analytics for the corporate insurance sector.

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Tackling Key Challenges in Client Requirements in the Insurance Sector

Using our expertise in development, machine learning and artificial intelligence, in partnership with The University of Plymouth, we will research and develop ways to use machine learning and AI to gain more accurate assessment of future risk.

This will also allow the leveraging of existing data architectures to model the relationships and interactions to mitigate risk, and to derive true value from the large volumes of datasets collated by business clients.

Machine Learning Project
Machine Learning Project

Machine Learning: The Future for the Insurance Industry

“This is a fantastic opportunity to explore how machine learning can be applied to predict future risks at a level of precision that was unthinkable just a few years ago. It feeds into everything from being able to take the right actions early to reduce risks and create more accurate costings, both in terms of insurance policies and reducing future claims, to more accurate assessment of emerging risks, be that in households or across business.”

Jon Stace, Principal Technical Consultant, Software Solved

Innovate UK Machine Learning Project Team

The Software Solved/Plymouth University Team

If you would like more information on Software Solved and machine learning or if you would be keen to participate in the project, please call: 01392 453333 or email

Machine Learning: What’s it all About?
Machine Learning: What’s it all About? 1024 682 Ashley Workman

Following on from other blogs that my colleagues have written, I also attended the Tech Exeter conference. The session I was most interested in was the talk regarding Machine Learning, AI and the Brain by Samantha Adams.

When thinking about AI and Machine Learning it is easy to be caught up thinking about the bleeding edge uses that often dominate the tech news. Examples such as self-driving cars, AlphaGo and digital assistants (Siri, Alexa, Cortana) are usually the first things that come to mind. While these technologies are exciting and can change the way we work in the future, as things stand, they are not going to revolutionize the way businesses work overnight.

What is Machine Learning?

So you may think why consider these technologies when their use cases are very restricted or still in development? In this slot I consider Machine Learning to reside, while Machine Learning can be a key part of an AI’s toolkit it is a distinct subset within the field of AI. Machine Learning, at its core, is training a computer based on some sample data how to produce a correct and useful extrapolation from said data. In concrete terms, this means that we can train a computer to perform tasks such as predicting user load on a website, highlight anomalous activity within a website, highlight anomalous performance of servers and predict how systems might need to scale in the future. All of these use cases are relevant and useful to businesses of today; Machine Learning can be used to trivialize many analysis tasks that would have previously been performed by an individual. Alternatively it can be used to support the decisions of professionals who do not have the time to study as many examples as a computer can, think medical uses.

What data to I need to utilise Machine Learning?

The main factor that predicts how well Machine Learning can do its job is the data set that it is fed. It is surprising then to learn that a lot of this data you will already have at hand or will be able to generate very quickly. For example, a proof of concept we completed recently involved using the ElasticSearch stack to create a dashboard of information using their Kibana technology. This dashboard was created by automatically feeding the logs generated by the webserver into the ElasticSearch stack, from here we can look to integrate their new Machine Learning tools to do some trend analysis and automated alerts based on live data. In this example we could instruct the Machine Learning package to alert us when we’re getting more requests than usual, when the time to complete requests is higher than usual or where there may be an attempt to gain unauthorized access to the site. This example illustrates how we can quickly derive useful information from large datasets using Machine Learning, but this is just one specific example. There are many other use cases including deciding if a tumour is malignant or benign from a brain scan or classifying images based on what they contain.

In Summary

Machine Learning is:

  • Not a technology of the future, it is already useful!
  • Great at parsing very large datasets, many more than any human can achieve, and making decisions based on the provided example and knowledge gained from all previously processed examples.
  • Able to be trained to perform a wide variety of tasks.
  • Exciting!
Why Insurers Should Be Thinking About Business Intelligence
Why Insurers Should Be Thinking About Business Intelligence 150 150 Simon Hollingworth

When we think of Business Intelligence (BI) it is very easy to think of product focused business models. Dashboards and reports based on production figures, supply chains and sales numbers. But the benefits of BI are not limited to product-centric industries, the insurance industry is perfectly placed to gain huge advantages from implementing BI. After all, insurance companies are businesses like any other and at their core are seeking to reduce costs, increase revenue and boost profitability.

Becoming More Efficient With Business Intelligence

Using BI tools to get better insights and get ahead of competitors, can be critical in such a hotly contested industry. BI tools allow your staff to make better decisions, with the right data, driving efficiency improvements and therefore, boosting profitability.

If you think about it in terms of risk management – a concept central to the industry – the benefits are obvious. A Risk Management department that has access to up-to-date, detailed and accurate 360-degree risk profiles are far better placed to make better decisions regarding levels of risk.

It is also important to consider the value of BI in terms of regulation and compliance. Insurance has a significant level of regulation and compliance becomes much easier to monitor when you have the right tools for the job. Giving your staff the right data, in the right format at the right time means they can make decisions to keep you above board and in business.

Using Business Intelligence to Improve Customer Experience

Just as BI can make a real difference to how your company manages risk profiles, it can do the same for your clients. Customer portals are nothing new in the insurance industry, and the era of self-service is going nowhere. What insurance companies are not doing though, is allowing clients to access BI around their own risk profiles.

Putting yourself in the shoes of the user (good practice, by the way), it’s very easy to see just how useful this would be for clients. Being able to log into a client portal and see your risk profile, in real-time, means they are better placed to manage their own risk. This is good news for them, they’re at lower risk now, and great news for the insurance company as the chances of a pay-out are reduced significantly.

In an industry so hotly contested, improving customer experience and inspiring brand loyalty can be the difference you need, and BI could be the answer.

If you’d like to know more about how BI could improve your business, talk to us today. We’d love to hear from you.

If you’d like to know more about our Business Intelligence services, click here.