Mark Hibberd

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Chief Technical Architect at Ambiata
mth.io
 @markhibberd

Mark Hibberd spends his time working on large-scale data and machine learning problems for Ambiata. Mark takes software development seriously. Valuing correctness and reliability, he is constantly looking to learn tools and techniques to support these goals.

This approach has led to a history of building teams that utilise purely-functional programming techniques to help deliver robust products.

Videos

YOW! West 2016 Mark Hibberd – Turning Technical Debt into Monetary Debt: Price Aware Architecture

Programming in the Large: Architecture and Experimentation by Mark Hibberd – YOW! 2014

YOW! Lambda Jam 2013 – Greg Davis & Mark Hibberd – Haskell in Production

YOW! Lambda Jam 2013 – Mark Hibberd & Tony Morris – Zippers, Comonads & Data Structures in Scala

YOW! Lambda Jam 2013 – Mark Hibberd – Patterns in Types: A Look at Reader, Writer & State in Scala

YOW! Lambda Jam 2013 – Mark Hibberd / Tony Morris – Argonaut – Purely-Functional JSON in Scala

Failure: Or the Unexpected Virtue of Functional Programming by Mark Hibberd – YOW! Lambda Jam 2015

YOW! 2014 Sydney

Programming in the Large: Architecture and Experimentation

TALK – VIEW SLIDES

Building robust, quality systems is hard. We trade off organizational issues against technical decisions; the ability to deliver quickly against our ability to change; and the ability to build systems easily against the ability to run those systems in production. However, good architectural decisions can free us to choose the right tools and techniques, allowing us to manage these challenges and concentrate on solving real problems rather than our made up ones.

In this talk, we will run through some stereotypical projects, come to terms with legacy systems, and look at the properties of robust architectures. In particular we are interested in how architectures lend themselves to experimentation and change in terms of both function and technology.

We will attempt to ground the discussion with examples from my past projects. Looking at where things have worked well and probably of more interest, where they really have not.