Megan Cartwright's guest appearances

Megan Cartwright

The InfoQ Podcast

Anurag Gupta

Adrian Cockcroft

Greg Murphy

Grady Booch

Bryan Liles

Lin Clark

Cathy O'Neil

Mike Lee

Michael Feathers

Arun Gupta

Anastasiia Voitova

David Wells

Guy Podjarny

Kolton Andrus

Kelsey Hightower

OwlTail

Today on the InfoQ Podcast, Wes speaks with ThirdLove’s Megan Cartwright. Megan is the Director of Data Science for the personalized bra company. In the podcast, Megan first discusses why their customers need a more personal experience and how their using technology to help. She focuses quite a bit of time in the podcast discussing how the team got to an early MVP and then how they did the same for getting to an early machine learning MVP for product recommendations. In this later part, she discusses decisions they made on what data to use, how to get the solution into production quickly, how to update/train new models, and where they needed help. It’s a real early stage startup story of a lean team leveraging machine learning to get to a practical recommendations solution in a very short timeframe.Why listen to this podcast: - The experience for women selecting bras is poor experience characterized by awkward fitting experiences and an often uncomfortable product that may not even fit correctly. ThirdLove is a company built to serve this market. - ThirdLove took a lean approach to develop their architecture. It’s built with the Parse backend. The leveraged Shopify to build the site. The company’s first recommender system used a rules engine embedded into the front end. After that, they moved to a machine learning MVP with a Python recommender service that used a Random Forest algorithm in SciKit-Learn.  - Despite having the data for 10 million surveys, the first algorithms only need about 100K records to be trained. The takeaway is you don’t have to have huge amounts of data to get started with machine learning. - To initially deploy their ML solution, ThirdLove first shadowed all traffic through the algorithm and then compared it to what was being output by the rules engine. Using this along with information on the full customer order lifecycle, they validated the ML solution worked correctly and outperformed the rules engine. - ThirdLove’s machine learning story shows that you move towards a machine learning solution quickly by leveraging your own network and using tools that may already familiar to your team.More on this: Quick scan our curated show notes on InfoQ https://bit.ly/2G9RnQnYou can also subscribe to the InfoQ newsletter to receive weekly updates on the hottest topics from professional software development. bit.ly/24x3IVqSubscribe: www.youtube.com/infoqLike InfoQ on Facebook: bit.ly/2jmlyG8Follow on Twitter: twitter.com/InfoQFollow on LinkedIn: www.linkedin.com/company/infoqCheck the landing page on InfoQ: https://bit.ly/2G9RnQn

Today on the InfoQ Podcast, Wes speaks with ThirdLove’s Megan Cartwright. Megan is the Director of Data Science for the personalized bra company. In the podcast, Megan first discusses why their customers need a more personal experience and how their using technology to help. She focuses quite a bit of time in the podcast discussing how the team got to an early MVP and then how they did the same for getting to an early machine learning MVP for product recommendations. In this later part, she discusses decisions they made on what data to use, how to get the solution into production quickly, how to update/train new models, and where they needed help. It’s a real early stage startup story of a lean team leveraging machine learning to get to a practical recommendations solution in a very short timeframe. Why listen to this podcast: - The experience for women selecting bras is poor experience characterized by awkward fitting experiences and an often uncomfortable product that may not even fit correctly. ThirdLove is a company built to serve this market. - ThirdLove took a lean approach to develop their architecture. It’s built with the Parse backend. The leveraged Shopify to build the site. The company’s first recommender system used a rules engine embedded into the front end. After that, they moved to a machine learning MVP with a Python recommender service that used a Random Forest algorithm in SciKit-Learn. - Despite having the data for 10 million surveys, the first algorithms only need about 100K records to be trained. The takeaway is you don’t have to have huge amounts of data to get started with machine learning. - To initially deploy their ML solution, ThirdLove first shadowed all traffic through the algorithm and then compared it to what was being output by the rules engine. Using this along with information on the full customer order lifecycle, they validated the ML solution worked correctly and outperformed the rules engine. - ThirdLove’s machine learning story shows that you move towards a machine learning solution quickly by leveraging your own network and using tools that may already familiar to your team. More on this: Quick scan our curated show notes on InfoQ <a href="https://bit.ly/2G9RnQn" rel="nofollow" target="_blank">https://bit.ly/2G9RnQn</a> You can also subscribe to the InfoQ newsletter to receive weekly updates on the hottest topics from professional software development. <a href="http://bit.ly/24x3IVq" rel="nofollow" target="_blank">bit.ly/24x3IVq</a> Subscribe: <a href="http://www.youtube.com/infoq" rel="nofollow" target="_blank">www.youtube.com/infoq</a> Like InfoQ on Facebook: <a href="http://bit.ly/2jmlyG8" rel="nofollow" target="_blank">bit.ly/2jmlyG8</a> Follow on Twitter: <a href="http://twitter.com/InfoQ" rel="nofollow" target="_blank">twitter.com/InfoQ</a> Follow on LinkedIn: <a href="http://www.linkedin.com/company/infoq" rel="nofollow" target="_blank">www.linkedin.com/company/infoq</a> Check the landing page on InfoQ: <a href="https://bit.ly/2G9RnQn" rel="nofollow" target="_blank">https://bit.ly/2G9RnQn</a>

Megan Cartwright on Building a Machine Learning MVP at an Early Stage Startup