Mathias Brandewinder

Mathias Brandewinder has been developing software for about 10 years, and loving every minute of it, except maybe for a few release days. His language of choice was C#, until he discovered F# and fell in love with it. He enjoys arguing about code and how to make it better, and gets very excited when discussing TDD or functional programming.

His other professional interests include machine learning and applied math. Mathias is a Microsoft F# MVP, author of Machine Learning Projects for .NET Developers (Apress), and the founder of Clear Lines Consulting. He is based in San Francisco, blogs at www.brandewinder.com, and can be found on Twitter as @brandewinder.


NewCrafts 2017

 

Serverless F# with Azure Functions: fsibot goes nano-services

  • talk
  • Functional
  • MachineLearning
  • ServerLess

Feeling a bit left behind by the whole micro-services hubbub? Fear not! This is your chance to get ahead of the curve, skipping straight to serverless nano-services, using Azure Functions. In this talk, we will cut through the buzzwords, and explain what Azure Functions are, why you should care, and how beautifully they fit with a functional style of development. We will demonstrate the benefits of the approach on a real-world example, illustrating along the way some patterns, benefits and gotchas.

Room: Hopper - Time: 5/19/2017 4:00:00 PM


NewCrafts 2016

 

Agile experiments in Machine Learning

  • talk
  • Functional
  • MachineLearning
  • Agile

Just like traditional applications development, machine learning involves writing code. One aspect where the two differ is the workflow. While software development follows a fairly linear process (design, develop, and deploy a feature), machine learning is a different beast. You work on a single feature, which is never 100% complete. You constantly run experiments, and re-design your model in depth at a rapid pace. Traditional tests are entirely useless. Validating whether you are on the right track takes minutes, if not hours.

In this talk, we will take the example of a Machine Learning competition we recently participated in, the Kaggle Home Depot competition, to illustrate what doing Machine Learning looks like. We will explain the challenges we faced, and how we tackled them, setting up a harness to easily create and run experiments, while keeping our sanity. We will also draw comparisons with traditional software development, and highlight how some ideas translate from one context to the other, adapted to different constraints.

Room: Orval - Time: 5/12/2016 1:45:00 PM