Govind Chandrasekhar

Attribute Extraction from ecommerce data - the generation of structured fields from unstructured text - is a popular product offering of ours. Our customers use it to improve the quality of their search

Weak supervision and active learning - a couple of key underutilized ideas for when the performance of your ML algorithms plateau, and the marginal utility of further investment in the project drops

An Ecommerce knowledge graph built from Semantics3's Universal Product Catalog. Useful for exploring supply chain insights, brand profiles, compatible looks and more. Made possible by our crawling and extraction engines.

Imports, exports and tariffs are quite the theme in the news these days, be it in the context of Brexit, the US-China trade war or the Iran nuclear deal. Executive decisions on what

Of the thousands of attributes that we handle while curating product catalogs, the hardest and perhaps most important attribute is brand. Consumers often begin their searches with brand names of the products they're

In this second of two posts about data quality, I'd like to delve into the challenge of building and maintaining evolving datasets, i.e., datasets that are function of changing inputs and fuzzy

In this first of two posts about data quality, I'd like to delve into the challenge of building and maintaining evolving datasets, i.e., datasets that are function of changing inputs and fuzzy

There is a significant disconnect between the perception and reality of how enterprise AI products are built.The narrative seems to be that given a business problem, the data science team sets about

Slides & video from my talk @ Fifth Elephant

And its implications for PMs, designers, salespeople and data scientists