Mark Hibberd


Chief Technical Architect at Ambiata

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.


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! 2016 Melbourne

Lab to Factory: Robust Machine Learning Systems

Data-driven systems and machine learning continue to be a significant trend across our industry. However, most attempts at these systems face serious difficulties due the tension between the clean, controlled, lab environments where statisticians apply their skills, and the messy unpredictable, production environments where we want to apply their results at scale.

In this talk, we will provide an overview of the machine learning landscape, with an emphasis on the distinction between machine learning as a scientific practice and the larger concept of machine learning systems. Using this base, we will walk through the challenges of taking machine learning out of the lab and applying it successfully in an industrial setting.

By the conclusion of this talk, the audience should take away a better understanding of machine learning as a practice, together with an idea of what it takes to build and deploy machine-learning systems in an environment that deals with real customers and data at scale.


Machine Learning, Data, Scale, Deployment