A playbook for building ML powered products, teams and businesses Building and selling machine learning (ML) products is hard. The underlying technology keeps evolving, requiring

Attribute Extraction from ecommerce data – the generation of structured fields from unstructured text – is a popular product offering of ours. Our customers use it

Writing crawlers to extract data from websites is a seemingly intractable problem. The issue is that while it’s easy to build a one-off crawler, writing

Here’s a standard storyline I’ve seen played out across organizations many times over: Lots of wasted resources. But critically, opportunities lost. Why does this happen, even

Over the past 7 years, we’ve built an extensive Universal Product Catalog, by curating and understanding public data from across the public e-commerce web. This

A couple of weeks ago, I posted a two part series detailing how we do data QA at Semantics3. In the days that followed, I’ve had people get

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

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

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

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

Three slides from Benedict Evans’ brilliant talk, The End of the Beginning, really caught my attention. Across industries, machine learning is helping us get to successive

Slides & video from my talk @ Fifth Elephant Recently, I gave a talk at Fifth Elephant, a “conference on big data and machine learning”. The

Product matching is a challenging data-science problem that we’ve been battling for several years at Semantics3. The variety of concepts and nuances that need to

And its implications for PMs, designers, salespeople and data scientists Garbage-In-Garbage-Out is the idea that the output of an algorithm, or any computer function for

And why Periscope Should’ve Held Out for a Little Longer Spoiler Alert: This article references a recent episode of the show Silicon Valley. It only refers

Working on data-science problems can be both exhilarating and frustrating. Exhilarating because the occasional insight that boosts your algorithm’s performance can leave you with a

And a peek into AWS’s latest playbook In his 2017 shareholder letter, Jeff Bezos hinted at a new wave of AWS value-add products — AI-powered APIs. “Amazon Lex

Artificial intelligence. Chatbots. Voice search. Virtual reality. Self-driving cars. Technology in 2017 and beyond sure promises to be exciting for consumers. For tech-centric businesses, these

Part 2 of a three-part series on the “Future of Ecommerce Search” Artificial intelligence. Chatbots. Voice search. Virtual reality. Self-driving cars. Technology in 2017 and

Part 1 of a three-part series on the “Future of Ecommerce Search” Artificial intelligence. Chatbots. Voice search. Virtual reality. Self-driving cars. Technology in 2017 and

Move over Nielsen! Gathering data about how your FMCG brand is performing can be terribly hard. If you’ve been through the grind, you’re probably used to