Air conditioning was leaking thru the ceiling tiles.
Stitch Fix is a clothing retailer in the United States, and we use technology and data science to help customers find the clothing they like. Earlier, I was chief engineer for about six-and-a-half years at eBay, where I helped our teams build multiple generations of search infrastructure.
If you have ever gone to eBay and found something that you liked then, great, my team did a good job. Let me start with a little bit about Stitch Fix, because that informs the lessons and the techniques of our breaking monoliths into microservices. Stitch Fix is the reverse of the standard clothing retailer.
Rather than shop online or go to a store yourself, what if you had an expert do it for you? We ask you to fill out a really detailed style profile about yourself, consisting of 60 to 70 questions, which might take you 20 to 30 minutes.
We ask your size, height, weight, what styles you like, if you want to flaunt your arms, if you want to hide your hips… — we ask very detailed and personal things. Anybody in your life who knows how to choose clothes for you must know about you.
As a client, you have five items we deliver to your doorstep, hand-picked for you by one of 3, stylists around the country. You keep the things that you like, pay us for those, and return the rest for free.
A couple of things go on behind the scenes among both humans and machines. On the machine side, we look every night at every piece of inventory, reference that against every one of our clients, and compute a predicted probability of purchase.
That is, what is the conditional probability that Randy will keep this shirt that we send him. We have machine-learned models that we layer in an ensemble to compute those percentages, which compose a set of personalized algorithmic recommendations for each customer that go to the stylists.
As the stylist is essentially shopping for you, choosing those five items on your behalf, he or she is looking at those algorithmic recommendations and figuring out what to put in the box.
We need the humans to put together an outfit, which the machines are currently not able to do. All of this requires a ton of data. Interestingly and, I believe, uniquely, Stitch Fix has a one-to-one ratio between data science and engineering.
We have more than a hundred software engineers in the team that I work on and roughly 80 data scientists and algorithm developers that are doing all the data science. To my knowledge, this is a unique ratio in the industry.
What do we do with all of those data scientists? It turns out, if you are smart, it pays off. We use data analysis for inventory management: We use it to optimize logistics and selection of shipping carriers so that the goods arrive on your doorstep on the date you want, at minimal cost to us.
And we do some standard things, like demand prediction. We are a physical business: Unlike eBay and Google and a bunch of virtual businesses, if we guess wrong about demand, if demand is double what we expect, that is not a wonderful thing that we celebrate.
If we have double the number of clients, we should have double the number of warehouses, stylists, employees, and that kind of stuff. It is very important for us to get these things right. Again, the general model here is that we use humans for what the humans do best and machines for what the machines do best.
When you design a system at this scale, as I hope you do, you have a bunch of goals. We want scalability, so that as our business grows, we want the infrastructure to grow with it. We want the components to scale to load, to scale to the demands that we put on them.
Also, we want those components to be resilient, so we want the failures to be isolated and not cascade through the infrastructure. High-performing organizations with these kinds of requirements have some things to do.
The DevOps Handbook features research from Gene Kim, Nicole Forsgren, and others into the difference between high-performing organizations and lower-performing ones. Higher-performing organizations both move faster and are more stable.
The higher-performing organizations are doing multiple deploys a day, versus maybe one per month, and have a latency of less than an hour between committing code to the source control and to deployment, while in other organizations that might take a week. On the stability side, high-performing organizations recover from failure in an hour, versus maybe a day in a lower-performing organization.
And the rate of failures is lower. The frequency of a high-performing organization deploying, having it not go well, and having to roll back the deployment approaches zero, but slower organizations might have to do this half the time.
This is a big difference. It is not just the speed and the stability.Published Pages are visible to the public. Unpublished Pages are only visible to the people who manage the Page. Unpublishing your Page will hide it from the public, including the people who like your Page, and your Page won't be visible to the public until it's published again.
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I'm Randy Shoup, VP of engineering at Stitch Fix, and my background informs the following lessons about managing data in microservices. Stitch Fix is a clothing retailer in the United States, and.