Despite the hype around machine learning and AI, the lifecycle of ML models often end in Kaggle competitions, hackathons and proof of concepts. Very few make it to production because individuals and teams inevitably encounter impediments in deployments, model management, and reproducibility, just to name a few. In this talk, we will share principles and practices on how we can overcome these challenges and enable teams to iteratively deliver ML solutions.
David Tan is a Software Engineer at ThoughtWorks and a data science enthusiast. He was working in the government sector in a non-technical role before he decided to embark on a career in software engineering. Over the last two years at ThoughtWorks, he has worked on several machine learning side projects on tasks such as stock market price prediction, fraud protection, and beer quantity image recognition. He is also a trainer for the ThoughtWorks JumpStart! program.
David is passionate about agile software development and knowledge sharing. During his free time he enjoys spending time with his family as a new dad.