Delivering ML Models the Safe and Sane Way

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.