November 11, 2016

Randy Shoup on Stitch Fix's Technology Stack, Data Science and Microservices

26 minutes

In this week's podcast QCon chair Wesley Reisz talks to Randy Shoup. Shoup is the vice president of engineering at Stitch Fix. Prior to Stitch Fix, he worked for Google as the director of engineering and cloud computing, CTO and co-founder of Shopilly, and chief engineer at Ebay.

Why listen to this podcast:

- Stitch Fix's business is a combination of art and science. Humans are much better with the machines, and the machines are much better with the humans.

- Stitch Fix has 60 engineers, with 80 data scientists and algorithm developers. This ratio of data science to engineering is unique.

- With Ruby-on-Rails on top of Postgres, the company maintains about 30 different applications on the same stack.

- The practice of Test Driven Development makes Continuous Delivery work, and the practice of having the same people build the code as those who operate the code makes both of these things much more powerful.

- Microservices gives feature velocity, the ability for individual teams to move quickly and independently of each other, and independent deployments.

- Microservices solve a scaling problem. They solve an organisational scaling problem, and a technological scaling problem. These are not the problems that you have early on in the startup.

- In the monolithic world, if you're not able to continue to vertically scale the application or the database or whatever your monolith is. And so for scaling reasons alone you might consider breaking it up into what we call microservices.

Notes and links can be found on http://bit.ly/randy-shoup-podcast

Data Science and Stitch Fix

1m:57s - Stitch Fix re-imagines retail, particularly for clothing. When you sign up, you fill out survey of the kinds of things that you like and you don't like, and we choose what we think you're going to enjoy based on the millions of customers that we have already. And we use a ton of data science in that process.

3m:00s - That goes into our algorithms and then our algorithms make personalised recommendations based on all the things we know about our other customers... there's a human element as well: we have 3,200 human stylists that are all around the United States and they choose the five items that go into the box [of clothing].

3m:29s - What we like is that this is a combination of art and science. Modern companies combine what machines are really good at, such as chugging through the 60 to 70 questions times the millions of customers, and combining that with the human element of the stylists, figuring out what things go together, what things are trending, what things are appropriate... Humans are much better with the machines, and the machines are much better with the humans.

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