Selecting ML Algorithms and Validating
ML Practitioners often have a dilemma in identifying the right ML Model for the problem space. In this talk, I will be going over the common questions that will help in narrowing down the right next step. Developed model will have to meet certain validation metrics. The next common question is how the validation metrics proposed by scientists will have to be explained to business leaders and help them decide if the model is eligible to deployed. The next step is to find mechanisms to develop and study the online validation metrics. Often online metrics of an ML model launched will require studying the results in Treatment-Control fashion. In this talk, I will describe common development practices that helps in A/B testing of experiments.
Lakshmi is an Applied Scientist with Amazon.She has been working with Amazon Machine Learning teams for the last 4.5 years. She had the chance to be part of Alexa's NLP team, Behavior Analytics (a causal Inference division in Amazon) and Amazon Music teams (improving the voice experience in Alexa).