Ep 4: ROI from ML at "Reasonable Scale" E-Commerce Companies with Ciro Greco
Building Things with Machine Learning
Release Date: 10/28/2022
Ep 6: Extracting Data from Old Documents with Rosa Lin, Founder, Tolstoy
Building Things with Machine Learning
Rosa Lin is the founder of Tolstoy [], which specializes in extracting data from documents. As I learned, this is a much tougher problem than traditional OCR! It requires a combination of deep learning and classic CV methods. Rosa also talks about her fascinating background as a journalist and her experience going through Y-Combinator. For more about this podcast, visit . For the video version including visual examples of Tolstoy's work, visit . 0:26: The problems Tolstoy solves: extracting data from documents like emails, news articles, forms, and handwritten notes and then...
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Ep 5: Discovering Pharmaceuticals with Machine Learning, with Ryan Emerson of A-Alpha Bio
Building Things with Machine Learning
A true “aha” conversation! Learn how deep learning techniques from natural language processing (NLP) are applied to drug discovery, specifically, protein to protein interactions. Includes a quick and dirty primer on just enough biology to understand the training data A-Alpha Bio uses for their ML models. For more episodes, visit . Show Notes: 0:37 - The basics of synthetic biology for machine learning practitioners 0:50 - What are proteins and why do they matter? 1:50 - A protein is a string of 20 amino acids… which means it starts looking like a Natural Language Processing...
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Ep 4: ROI from ML at "Reasonable Scale" E-Commerce Companies with Ciro Greco
Building Things with Machine Learning
Ciro Greco has built ML systems used at many named-brand retailers. In this episode, he gives us tips on getting value out of ML at “reasonable scale” companies with NLP and information retrieval. The concept of “reasonable scale” was one he returned to, and he clearly has a very nuanced understanding of that segment and how they are different from the hyper scale tech giants. He also brings advanced ideas like embeddings from NLP towards e-commerce personalization. For more episodes, visit . Show Notes: 1:36: Key differences in applying ML at “reasonable scale”...
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Ep 3: Applying ML to Cybersecurity, with Yihua Liao
Building Things with Machine Learning
Yihua Liao is Head of Data Science at Netskope, a next-generation cybersecurity firm. Yihua talks about using both CV and NLP to create novel cybersecurity features. Yihua Liao’s PhD research was on security and machine learning, and he previously worked at Microsoft, Facebook, Uber, and his own startup. For more information about this podcast, visit . Show Notes: 00:24 - How Netskope addresses cybersecurity. 00:57 - Netskope’s unique approach to cybersecurity through network traffic routing. 02:51 - The prior approach to cybersecurity: a focus on the physical perimeter and...
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Ep 2: Tedd Mann @ CollX
Building Things with Machine Learning
Ted tells us about applying machine learning to the field of baseball cards! 33% of Americans have trading cards, making this a very large addressable market. Learn some tips on scrappy ways to launch an app, and how similarity search powers one of the killer features of the CollX app. Key Moments: Building an application that works around the potential errors of an ML model (15:10). The data and ML behind his trading card valuation model, especially when recent transactions don’t exist. (18:30). Dealing with the latency inherent in ML and networking through the concept of...
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Ep 1: Tom Rikert @ Masterful AI
Building Things with Machine Learning
In this episode, I interview my colleague Tom Rikert at Masterful AI. Tom is building the "AutoML 2.0" platform for computer vision. We talk about the product for the first 10 minutes, and then spend some learning about his work at MIT CSAIL, which got him into robotics and computer vision, as well as his experiences selling a startup to Google and his time as a venture capitalist at Andreessen Horowitz and Nextworld Capital. To learn more about this podcast, visit . For a video version of this episode, visit .
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Trailer
Building Things with Machine Learning
Welcome to the Building Things with Machine Learning Podcast. Every episode, I’ll be interviewing someone who building really interesting products using machine learning. Our focus is really on applications: Medical diagnostics Autonomous vehicles & advanced driver assistance systems (ADAS) Geospatial analytics Media and Content analysis Manufacturing Logistics And AEC, Architecture / Engineering / Construction What you won’t get are coding tips or research papers. Although ML developers are definitely part of our audience, so are product managers and marketers and...
info_outlineCiro Greco has built ML systems used at many named-brand retailers. In this episode, he gives us tips on getting value out of ML at “reasonable scale” companies with NLP and information retrieval. The concept of “reasonable scale” was one he returned to, and he clearly has a very nuanced understanding of that segment and how they are different from the hyper scale tech giants. He also brings advanced ideas like embeddings from NLP towards e-commerce personalization.
For more episodes, visit https://yaoshiang.com/podcast.html.
Show Notes:
1:36: Key differences in applying ML at “reasonable scale” companies like major retailers where you can’t just “big-data” your way out of problems, compared to the hyper scale tech giants.
3:22: The basics of personalization: suggestions, search, recommendations, and categories.
4:38: A non-obvious challenge: how to personalize for non-logged-in users without a profile who visit infrequently.
9:00: Different incentives for reasonable scale vs hyper scale companies.
9:44: Getting your data right: data ingestion, data practices, organizing teams around data, transforming data, infrastructure for flexible data access, so that you can make developers productive when you have finite resources.
11:23: Learning from experience that data - replayability and replicitability - is more important than modeling.
12:58: Learnings from experiences at presenting at top tier conferences: so many published papers come from the hyper scale companies.
14:19: Taking session data and catalog data to create a “product to vector” embedding to personalize an experience.
19:20: Requirements on how to sell: the sales people must communicate to the “people who write the check” that data integration is a first class citizen, not a downstream task, to achieve ROI.
21:09: Dynamics of regulatory and privacy issues, and how to tackle them as an organization.
24:10: Ciro talks about his personal journey into ML, starting with a PhD in neuroscience and linguistics.
25:46: Early challenges in applying deep learning to NLP.
26:22: The “a ha” moment that led to Ciro’s first startup delivering search products.
27:55: Changes in the role of a data scientist over the past decade. From the role of PhDs who had to tackle problems with very little tooling, to today where there are so many tools available. And a shift towards understanding products and customers.
For the video version of this podcast, visit https://www.youtube.com/watch?v=F3e0UPqenwo