Why Machine Learning In Ad Tech Is Ready For Liftoff

Yunshi Zhao is a Machine Learning Engineer at Liftoff, a mobile app optimization platform for marketing and monetizing apps at scale. Her responsibilities range from researching and training models to deployment and monitoring models in production. She is also part of the diversity, equity, and inclusion (DEI) committee at Liftoff, focusing on representation in engineering. Before transitioning to startup life, she worked as a data scientist and aerospace engineer. Here, she talks about machine learning development, best practices, use cases, and ML in production.

What is Liftoff’s mission as a company and what made you want to join the team?

Liftoff Mobile is a technology and programmatic advertising company. The organization has a lot of products in different areas of the advertising technology ecosystem, especially in the wake of our merger with Vungle last year. But the main mission is to help mobile apps grow and monetize.

I really like the vertically integrated system where you get to do everything along the model lifecycle. At most companies, you’re hired to do data science and model development. But then you hand that off to a different engineer to deploy it. At Liftoff, the ML group does it all and that was really appealing to me.

How did you train for this role and what are your tips for anyone interested in transitioning into AI?

Luckily, my previous job in aerospace engineering used a lot of the same math, so I would say anyone with a strong math background would have an easier time making the transition to being a machine learning engineer (MLE). For the programming part, there are so many online resources to help ramp up on the software and there’s also such a big community of people you can ask for help. If you don’t have the math background, you can always start with something that’s not as heavy on the programming part. Data science and data analytics are good starting points and then you can slowly work your way up to MLE. I think of this progression as a video game, where you advance through all the different levels.

What vertical are you focused on at Liftoff and what does your day-to-day look like?

I work on the demand-side platform (DSP), which is a system that helps advertisers buy the right ad for the right price. Our team’s main job is to build conversion models and predict the probability of conversion in one of the down-funnel events. My day-to-day job really depends on whatever project I’m working on, but it usually involves kicking off model experiments. Sometimes before the model kickoff I will also work on our code base to update our model. I also produce code to update how we train the model and make code changes in the bidding path for the part of code we use in the model to bid on an ad. Liftoff has a strong documentation culture, so I do a lot of writing as well for any ideas I want to propose or thought experiments I want to share. I also meet with other teams to better understand the business metrics and how our model should behave in that business context.

Scalability is an important part of infrastructure, especially with your use case in advertising technology. What are some things to keep in mind for the scalability of data?

Our Kafka processes two gigabytes of data per second, which is a lot of data. Much of our system is built around knowing the data we need to process, and it’s a challenge to do feature analysis mainly because a lot of our system is built in-house and they have a narrowed use case. It worked really well for the original case we built it for and they made sure that everything was really fast—the position is fast and then the continued training is fast, but then we have a challenge with feature analysis. Since we have a large data set, it’s not easy to natively do any feature analysis like you might for other use cases. It’s definitely something we always talk about when we decide on any system in our company.

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