THKT —  Keynote Talk 2   (18-Oct-18   09:00—10:00)
Chair: C.H. Kuo, NSRRC, Hsinchu, Taiwan
Paper Title Page
Delivering Machine Learning Engineering in Scientific Research  
  • D. Chiu
    LargitData, Taipei, Taiwan
  As the use of machine learning to train a predictive model has become more and more popular, knowing how to build a model with a fixed dataset may not be news to most researchers nowadays. With a little programming training, any researcher can now easily create a model with just a few lines of coding with some state of the art programming languages and analysis tool. However, knowing how to build the model is not enough, if you wish your research can reach to a wider audience, it would be important to know the correct approach to deliver the learned model into a production environment. In this keynote speech, I will address how to bring machine learning model into the production environment from the perspective of a data scientist and engineer. I will cover the issues includes data preparation, model versioning, model deployment, continuous integration/delivery, model validation, how to choose evaluation metrics, some challenges when facing big data.