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.
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.
Ben is the CTO of Ambiata and once upon a time wrote code, built clusters and even compiled Haskell for GPUs.
Nowadays he spends his time figuring out the best way to combine the people and skills from both Ambiata’s sofware engineering and data science teams in order to build machine learning systems that have the best chance of working in the real world.
Ben likes to focus on the real world because it means focusing on delivering robust products to customers that solve problems they actually have.