Interpretable ML Models at Scale Aishwarya Agrawal, Py Data Global 2021

Interpretable ML Models at Scale Speaker: Aishwarya Agrawal Summary In this talk a participant can expect to understand the following: 1. Building a self service interpretable ML framework for stakeholders 2. Incorporating feedback and autoML workflows 3. Interpretable ML supporting early data, concept drift detection This talk will take a deep dive into the thought process involved in the system s design, the application and the importance of designing such. Description Building an interpretable system Summarized explanations of the predictions Running multiple inference models in production Lessons learnt while optimizing interpretable AI at scale With the increase in machine learning models being used in a variety of business applications, the need to make explainable ML inference is ever evident. Reducing risk, ethical ML, avoiding biases, debugging bad predictions etc are usually cited as key reasons for model interpretability. Making it interpretable i. e. app