Arpeely: Scaling an Innovative Data Science Platform Globally with a Small Local Team
Technology Category
- Analytics & Modeling - Machine Learning
- Infrastructure as a Service (IaaS) - Cloud Computing
Applicable Industries
- Education
- Equipment & Machinery
Applicable Functions
- Sales & Marketing
Use Cases
- Predictive Maintenance
- Traffic Monitoring
Services
- Cloud Planning, Design & Implementation Services
- Training
About The Customer
Arpeely is a data science startup based in Israel that uses machine learning and feature engineering to discover hidden opportunities in online advertising. The company processes dozens of billions of predictions daily and cherry-picks traffic based on in-app or post-conversion behavior KPIs. Arpeely is connected to the world’s largest advertising exchanges and achieved multimillion-dollar revenues in its first year of trading. By now, every single user in the U.S. has passed through Arpeely’s servers at some point, and it processes 20 billion ad impressions a day, while delivering millisecond predictions per ad view.
The Challenge
Arpeely, an Israeli ad-tech startup, aimed to revolutionize the media-buying process by leveraging machine learning and feature engineering techniques. The company sought to process billions of ad impressions daily and cherry-pick traffic based on in-app or post-conversion behavior KPIs. However, as a bootstrapped startup launched in 2017, Arpeely faced the challenge of managing global ad operations with a small team. The company needed a solution that would allow it to scale up quickly without having to invest heavily in developing complex services or expanding its team.
The Solution
Arpeely turned to Google Cloud to launch and scale its innovative ad-tech platform. Google Cloud provided the necessary tools and support for Arpeely to build and scale its operations, leveraging compute, data, and machine learning solutions. Arpeely started with App Engine, which allowed it to quickly iterate in the early days. As volumes scaled up, Arpeely moved on to the fully managed Google Kubernetes Engine (GKE) and BigQuery. BigQuery became the heart of Arpeely’s data warehouse, aggregating all its analytics, business metrics, and third-party integrations. The system automatically scaled up to deploy several hundred nodes as demand grew. Meanwhile, AutoML scaled the training of the machine learning models that Arpeely created, allowing it to continually fine-tune its bidding systems.
Operational Impact
Quantitative Benefit
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