Engineering Meets Statistics: Niloy Gupta’s Machine Learning Systems Drive Tech Forward [Video]

Machine Learning (ML)/Artificial Intelligence (AI) has its roots in classical statistics. Financial analysts use statistical models to predict returns on assets. Actuaries build models to analyze risk in the insurance industry. Weather forecasters use sophisticated statistical models to predict weather.

With the birth of the internet, there is a considerable demand to scale up training statistical models and serve predictions to millions of customers. These machine-learning systems must be accurate, reliable, fast, and cost-effective.

The level of accuracy depends on the industry vertical. Poor predictions in medicine, driving, and credit underwriting are less tolerated than ad targeting and e-commerce product recommendations.

Building systems that cost-effectively scale ML model development and deployment becomes the need of the hour. For Niloy Gupta, a staff machine learning engineer and tech lead at Attentive Mobile and co-founder of Lambent Logic, scaling such systems requires blending concepts from statistics, ML, software engineering, and product domain …