Ever wondered how Kaggle masters combine hundreds of different machine learning models to win modelling competitions? Ever wondered how to become ranked #1 on Kaggle? StackNet has helped me do that! StackNet is an open-source, scalable and automated meta-modelling framework that combines various supervised models to improve performance. Written in Java, this library automates many of the laborious aspects of building stacking models, so that you can focus on the important parts and move higher up the Kaggle leaderbo ards.

In another talk, Piotr Migdał gave us some tricks and tips about reproducibility and a number of best practices on team cooperation and model deployment. It's important to be aware that even a single untracked parameter tweak can lead to frustration and inefficiencies in the whole team. I can't resist to mention his good humor with the parallelism between Borja's Ecce Homo painting and model reproducibility issues.

From October 2016 to January 2017, the Outbrain Click Prediction competition challenged Kagglers to navigate a huge dataset of personalized website content recommendations with billions of data points to predict which links users would click on. In this winners' interview, team brain-afk shares a deep dive into their second place strategy in this competition where heavy feature engineering gave a competitive edge over stacking methods. Darragh Hanley: I am a part time OMSCS student at Georgia Tech and a data scientist at Optum, using AI to improve healthcare. Marios Michailidis: I am a Part-Time PhD student at UCL, data science manager at dunnhumby and fervent Kaggler. Mathias Müller: I have a Master's in computer science (focus areas cognitive robotics and AI) and I'm working as a machine learning engineer at FSD. Alexey Noskov: I have an MSc in computer science and work as a software engineer at Evil Martians.