How we brought machine learning awareness to the business
Daniel Wellington is a Swedish brand founded in 2011. Since its inception, it has sold over 11 million watches and established itself as one of the fastest growing and most beloved brands in the watch industry.
Most people know how influencer marketing helped us become a global company, but what many don’t know is how our use of technology made that possible.
The first system we developed enabled us to reach influencers across the globe at scale. Then, we created our own e-commerce platform to be able to speak to our customers the way we wanted, and we built a complex ecosystem with B2B, B2C, and marketing channels working together.
We develop our services and applications on the AWS cloud platform and make extensive use of all of its amazing serverless features, neatly matched with microservice based architectures.
Our design principle is serverless first. If serverless is not available or practical, containers are recommended, EC2 is legacy. JavaScript, PHP, and Go are some of our languages of choice.
As a global tech-company that has disrupted an entire industry, we are constantly learning and finding our own way of doing business. Our latest venture is to understand how machine learning can help us develop business value.
We started by looking into how we could build our own machine learning models, and make it recognize patterns and objects. Early on, we became aware of the complexity and scope of the effort, which made us change our approach.
In order to move fast and agile and still get the best results possible, we looked at what the open source community has already created. Our goal was to find a way for machine learning to solve day to day problems and learn new ways to improve our business.
Our first idea became DW Concierge, a fork of the Doorman community project, that we rewrote for the most part in Go, adding custom features.
The AWS Deeplens runs parts of the DW Concierge code to identify people within its range. When a person is found, it sends the image to an S3 bucket, triggering the execution of another DW Concierge module which makes use of AWS’s facial recognition service.
If the person is known, the command “open” will be sent back to Deeplens where DW Concierge will send a signal to a USB relay connected to a pre-existing “Open door” button, letting the person in without the need to use their access tag.
Here is a demo of the setup, note that it usually takes 2–4 s for installation to work, but in this case, it went surprisingly quickly.
Feel free to clone it and use it at your work place! We would love you to fork the project or even better, send a pull request with new features!
https://github.com/dwtechnologies/concierge
