Talk

Keep it Clean: Why Bad Data Ruins Projects and How to Fix it

Published

Oct 24, 2019

Author

Dr. Phil Winder

CEO

Slides Abstract The Internet is full of examples of how to train models. But the reality is that industrial projects spend the majority of the time working with data. The largest improvements in performance can often be found through improving the underlying data. Bad data is costing the US economy an estimated 3.1 trillion Dollars and approximately 27% of data is flawed in the world’s top companies. Bad data also contributes to the failure of many Data Science projects.

Life and Death Decisions: Testing Data Science

Published

Apr 25, 2018

Author

Abstract We live in a world where decisions are being made by software. From mortgage applications to driverless vehicles, the results can be life-changing. But the benefits of automation are clear. If businesses use data science to automate decisions they will become more productive and more profitable. So the question becomes: how can we be sure that these algorithms make the best decisions? How can we prove that an autonomous vehicle will make the right decision when life depends on it?

Research-Driven Development: Improve the Software You Love While Staying Productive

Published

Oct 16, 2017

Author

Slides Abstract Have you ever wondered which parts of your job you love or hate? Chances are that like most developers you love learning and new problems to solve. You hate monotony and bureaucracy. You’ve probably put strategies in place to mitigate the things you don’t like. An anarchic development process like Agile, to reduce the amount of time in meetings. But have you ever thought about the way in which you approach learning and problem solving?

What is Artificial Intelligence?

Published

Oct 6, 2017

Author

Dr. Phil Winder

CEO

If you ask anyone what they think AI is, they’re probably going to talk about sci-fi. We sometimes even need to set the record straight during our AI consulting projects. Science fiction has been greatly influenced by the field of artificial intelligence, or A.I.

Probably the two most famous books about A.I. are I, Robot, released in 1950 by Isaac Asimov and 2001: A Space Odyssy, released in 1968 by Arthur C. Clarke.

I, Robot introduced the three laws of robotics. 1) A robot must not injure a human being, 2) a robot must obay the orders, except where the orders would conflict with the First Law and 3) a robot must protect its own existance as long as such protection does not conflict with the First or Second Laws.

2001: A Space Odyssey is a story about a psychopathic A.I. called HAL 9000 that intentionally tries to kill the humans on board a space station to save it’s own skin, in a sense.

But the history of AI stems back much further…

The Meaning of (Artificial) Life: A Prelude to What is Data Science?

Published

Oct 3, 2017

Author

Slides Abstract The Hitchhiker’s Guide says the meaning of life is 42. Considering that the field of Data Science is going through a period of exponential growth it too could soon find that the meaning of an artificial life is also 42. But if you are not involved on a day-to-day basis, the expansion can seem bewildering. The story of how disparate disciplines have combined to produce Data Science is fascinating.

Cloud-Native Data Science: Turning Data-Oriented Business Problems Into Scalable Solutions

Published

Jun 12, 2017

Author

Abstract The proliferation of Data Science is largely due to: ubiquitous data, increasing computational power and industry acceptance that solutions are an asset. Data Science applications are no longer a simple dataset on a single laptop. In a recent project, we help develop a novel cloud-native machine learning service. It is unique in that problems are packaged as containers and submitted to the cloud for processing. This enables users to distribute and scale their models easily.

Monitor My Socks: Using Prometheus in a Polyglot Open Source Microservices Reference Architecture

Published

Apr 29, 2017

Author

Abstract This presentation describes how Prometheus was integrated into a polyglot microservices application. We will use the “Sock Shop”, a cloud-native reference microservices architecture to demonstrate some of the best practices and pitfalls of attempting to unify monitoring in real life. Attendees will be able to use this application as a reference point, or as a real life starting point for their own applications. Specifically, we will cover: Integrating Prometheus in Java/Go/Node.

Exploring Microservice Security in an Open-Source Sock Shop

Published

Nov 15, 2016

Author

Slides Abstract Microservices are often lamented as “providing enough rope to hang yourself”, which gives the impression that microservices are inherently insecure. But if we do microservices right, we can improve security with a range of measures all designed to prevent further intrusion and disruption. In this talk, you will discover a reference microservices architecture - the sock shop - which we will abuse in order to investigate microservice security on the Kubernetes orchestrator and Weave Net, a software-defined networking product from Weaveworks.