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Photo by Andrik Langfield on Unsplash

This is the first in a multi-part series aimed at how organisations can manage and optimise costs to get the best of their cloud infrastructure. At Momenton, we place a strong focus on leveraging cloud whilst also seeking to optimise cost management.

Public cloud adoption continues to accelerate with cloud spending rising as companies adopt multi-cloud strategies and expand into the cloud. Public cloud is now a significant line item in IT budgets, especially among larger organisations. Cloud spends are expected to increase by half in the next year and are typically over budget¹.

COVID-19 will further increase cloud use. Extra capacity will be needed to meet increased demand as online usage grows. Migration from data centres to cloud will accelerate in response to reduced headcount, difficulties in accessing data centre facilities and delays in hardware supply chains¹. …


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Photo by Fancycrave on Unsplash

Google Cloud Platform gives you easy access to computing resources and services that you can use to build and scale your business. These come at a cost and it is important to actively monitor how much you are spending to keep your costs under control. You can do this is by using Google Cloud Billing alerts. We are going to show how to use these alerts to send your current budget’s spending to a Slack channel via Slack notifications. …


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Photo by Raphael Schaller on Unsplash

TeX is the lingua franca of scientific publishing. It is powerful and precise, a 70’s throwback that is a testament to its creator’s Donald Knuth genius. Whilst TeX is great, it can be very complex once you start doing more than just simple typesetting and there is not a lot of documentation to help.

I wanted to use TeX to typeset some diagrams of neural networks for an upcoming paper. Nothing too complex, a few symbols and a number of circles with directed lines connecting them, see Figure 1 below. I managed to ‘TeX’ the diagrams and was happy with the results, however it was a non-trivial process. …


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Photo by Green Chameleon on Unsplash

Deep neural networks are an important class of neural networks that have been applied to numerous machine learning areas, such as natural language processing [WE:2016], computer vision [KR:2012] and speech recognition [HI:2012]. Training of such networks is often successfully performed by minimising a high-dimensional non-convex objective function. In a theoretical sense, we have only scratched the surface of this optimisation problem and a number of important questions remain to be proved about its behaviour.

A recent paper “Gradient Descent Finds Global Minima of Deep Neural Networks” by Du, Lee, Li, Wang and Zhai [DU:2018] sheds light on two unexplained behaviours of deep neural…


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Photo by Mika Baumeister on Unsplash

DataProc is Google Cloud’s Apache Hadoop managed service. It is a quick, easy and relatively inexpensive way to build Hadoop clusters from a few instances to hundred of thousands. Whilst Hadoop excels at processing big data, we will look at its ability to perform calculations for a class of high throughput and high latency computations. This opens up the ability to perform numerical experiments at a scale that previously would have been difficult or restricted to the domain of supercomputers.

Recently Andrew Sutherland from MIT used Google cloud platform “to explore generalisations of the Sato-Tate Conjecture and the conjecture of Birch and Swinnerton-Dyer to curves of higher genus”[1]. He used 580,000 cores and broke the record for largest ever Compute Engine job. …


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“smiley paint on gray ground in front of people” by Nathan Dumlao on Unsplash

AWS’s Comprehend provides a natural language processing service that uses machine learning to identify a text’s language, extract key phrases, understand sentiment (how positive or negative the text is) and automatically organise text by topic. You can easily analyse text and apply the results in a wide range of applications, finding insights and relationships in text. No need to understand how it works, just call an API and get the results. Simple?

Machine learning algorithms not only need to be accurate but also understandable and explainable. It is important to test how they behave for your specified domain.

I was interested in analysing user feedback. This can include emoticons and often does. I was curious how Comprehend would handle these ubiquitous little characters. …

About

Darren Reading

Data Scientist/Cloud Architect @ Momenton. Ph.D in Computational Mathematics

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