Posted by Darren Halket
Fri, Feb 3, 2017

Following our introduction to muvr & follow up blog post earlier this week we are excited to bring you our full documentary on muvr, in which our team explain the thinking behind the App and it's implementation through IOT and Machine Learning.

Posted by Darren Halket
Wed, Feb 1, 2017

Following our introduction to muvr blog post earlier this week we thought this a good point to share more of our thinking behind the creation of muvr, along with the opportunites that a project of this nature provides for a business. 

Posted by Darren Halket
Mon, Jan 30, 2017

In this blog we will show how machine learning can bring great benefits in the development of fitness applications and how the culture at Cake Solutions fosters such innovation.

Posted by Matt Roberts
Wed, Dec 7, 2016

This blog post looks at how to generate polynomial expressions automatically from a given set of expected outputs using genetic programming (a form of supervised machine learning).

Posted by Martin Zapletal
Sun, Nov 20, 2016

Introduction

In this series of posts I will discuss the evolution of machine learning algorithms with regards to scaling and performance. We will start with a naive implementation and progress to more advanced solutions finally reaching state of the art implementations, similar to what companies like Google, Netflix and others use for their data pipelines, recommendation systems or machine learning. A variety of topics will be discussed, from basics of ML, different programming models, impact of distributed environment, specifics of machine learning algorithms as compared to common business applications and much more. For those not particularly interested in machine learning the concepts discussed are chosen carefully to apply to a wide range of applications and ML itself is chosen as a good example.

In my previous blog post we looked into neural networks, their training and investigated a trivial single threaded object oriented implementation. The result was a working example that was, however, not useful in many real world scenarios for its poor performance. With large amounts of data such approach is extremely wasteful and we can achieve vastly better performance through parallelization.

Posted by Martin Zapletal
Mon, Nov 14, 2016

Welcome to a new edition of #ThisWeekInScala!

This blog aims to keep you up to date with the latest news from the world of Scala and Reactive programming.

Posted by Martin Zapletal
Sat, Oct 1, 2016

Introduction

In this series of posts I will discuss the evolution of machine learning algorithms with regards to scaling and performance. We will start with a naive implementation and progress to more advanced solutions finally reaching state of the art implementations, similar to what companies like Google, Netflix and others use for their data pipelines, recommendation systems or machine learning. A variety of topics will be discussed, from basics of ML, different programming models, impact of distributed environment, specifics of machine learning algorithms as compared to common business applications and much more. For those not particularly interested in machine learning the concepts discussed are chosen carefully to apply to a wide range of applications and ML itself is chosen as a good example.

Although very old concepts, the importance of big data analytics and machine learning is steadily increasing. One of the reasons is improving accessibility of tools, decreasing prices and therefore the ability to access, store, process and use large amounts of data. And data are key for many use cases, from optimizing standard business use cases to finding and opening new business opportunities to completely transforming businesses.

Throughout this series of blog posts we will touch on many topics from machine learning, functional programming, parallel programming to distributed systems theory. I will start with a brief introduction into the different programming models, followed by abstract description of single machine, parallel and distributed computation, common data processing architectures, pipelines and technology stacks before getting to the actual focus of the blog post. Feel free to skip to chapter Perceptron if you want.

Posted by Peter Evison
Mon, Jun 27, 2016

Scala Days 2016 in Europe is over for another year, but what an incredible three days! One thousand people attended the conference in Berlin, which, as last time was a perfect host. As always Cake had a strong presence, in terms of talks (see below) and the Cake stand, supported by 12 members of the Cake team. Oh and we must not forget the great coffee our friends from Noble Espresso provided the attendees!

If you missed Scala Days then do not fear, we will be appearing at a number of other conferences in the UK, Europe and North America. We will be attending  Scala World for example, which was a great success last year; the feedback I received from our engineering team was fantastic!  We encourage you all to attend this year in September, details can be found here

We are the main sponsor for the Reactive Summit  in Austin (TX) which promises to bring together the world of reactive programming.

We will also be sponsoring the Scala eXchange in London in December which promises to be Europe's largest Scala Conference!

Posted by Peter Evison
Thu, Jun 2, 2016

Well as we wave goodbye to Scala Days NYC and prepare for Scala Days Berlin its time for us to share our thoughts and look back at the event. What better way to do that than a short video documentary! I hope you enjoy…

Posted by Petr Zapletal
Sun, Jan 3, 2016

Welcome to new edition of #ThisWeekInScala!

This blog aims to keep you up to date with the latest news from the world of Scala and Reactive programming.

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