The State of Ecommerce - 2019 Report We analyzed 138 million dotcom sites, with a focus on ~6 million ecommerce sites. Here's what we found.
Principles for Managing Data & Product Quality Measure everything, set realistic targets, Think statistically and 6 other principles for managing product/data quality
Mapping the Universe of E-commerce Brands 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
How we do Data QA @ Semantics3: Processes & Humans-in-the-Loop (Part 2) 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
How we do Data QA @ Semantics3: Statistics & Algorithms (Part 1) 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
How to Launch and Maintain Enterprise AI Products 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
Deriving Meaning through Machine Learning: The Next Chapter in Retail Three slides from Benedict Evans' brilliant talk, The End of the Beginning, really caught my attention.The Old and the NewAcross industries, machine learning is helping us get to successive levels of meaning
5 Lessons I’ve Learned Tackling Product Matching for E-commerce Slides & video from my talk @ Fifth Elephant
The GIGO Principle in Machine Learning And its implications for PMs, designers, salespeople and data scientists
“Hot Dog and a Not Hot Dog”: The Distinction Matters And why Periscope Should’ve Held Out for a Little Longer
Questions & Intuition for Tackling Deep Learning Problems Working on data-science problems can be both exhilarating and frustrating. Exhilarating because the occasional insight that boosts your…
engineering Debugging Neural Networks: A Checklist You’ve framed your problem, prepared your datasets, designed your models and revved up your GPUs. With bated breath, you start training…
engineering Geosearch Support for Limestone (Node.js Sphinx Connector) Limestone, a Node.js connector to Sphinx search server, is a quite handy library written by Serge Shirokov.
engineering How We Built an iOS App, an Android App and a Node.js API in 20 Hours My typical app development workflow involves brainstorming, idea formation, validation, feature selection, design iteration and eventually…
