Recently, we packed our 30-person developer team into a bus and drove them into the middle of nowhere for a one-day offsite. We wanted to take ourselves out of the day-to-day working environment, and focus on learning—and teaching—new skills and knowhow. Smartly.io’s first ever Engineer.io was a success.
Automation is becoming more and more prevalent in the world of online advertising. Online shopping sites may have tens of millions of products in their product catalogs, and making ads for these products manually is simply not feasible. A key part of automation is feed-based advertising, where ads get created automatically based on files containing product information such as names, descriptions, prices, pictures and so on. This blog post describes how we built the infrastructure to handle the volume of image rendering requests our advertising automation requires.
Python and R are some of the best open source tools for data science. They can be easily used for scripting and custom analysis but running them automatically as part of an online software requires more consideration. At Smartly.io, we’ve been using them both extensively. In this blog post, I’ll share some of our experiences of integrating them in production.
Finding the best way to allocate a campaign’s budget between multiple ad sets can be difficult and time-consuming. Our Predictive Budget Allocation, which was initially released last summer, uses machine learning to automate this work for you. In this blog post we'll look at recent improvements that make it even better. Using Predictive Budget Allocation remains as easy as it's always been: you only have to choose the goal that Predictive Budget Allocation should optimize towards.
Editor’s note: The original blog post was published in June 2015. We’ve since opened an office in Singapore, which made it possible for us to launch 24/5 customer service.