Use Case
How Coinbase Scales ML from Real-Time Fraud Prevention to Personalization
Coinbase, a leading cryptocurrency platform, uses machine learning for critical operations including fraud detection, personalization, and blockchain data analysis. Processing user actions in milliseconds and managing thousands of features across hundreds of pipelines, Coinbase needed a robust solution to scale their ML infrastructure. This led them to leverage Tecton's feature platform.
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Radically Easier: From Manual Pipelines to Streamlined Feature Engineering
When Coinbase first started with machine learning over eight years ago, their approach was entirely ad hoc, with teams building pipelines manually. While they attempted to build in-house solutions, they found feature engineering to be a common problem that was challenging to solve with a small team.
“What shines about Tecton is the feature engineering experience—that developer workflow,” explains Joseph McAllister, Senior Engineer on Coinbase’s machine learning platform team. “From the very beginning, when you’re onboarding a new data source and building a feature on Tecton, you’re working with production data, and that makes it really easy to rapidly iterate.”
The improved workflow has enabled more people to build streaming pipelines without requiring the same domain expertise as before, while individual engineers can iterate faster on their pipelines.
Self-Service Development: From Multiple Teams to Single Developer Ownership
Prior to Tecton, feature development often required multiple teams and handoffs. “There is no concept of one person designing a feature and prototyping it, and a separate team productionizing and maintaining that feature,” McAllister notes.
Now with Tecton, a single person can develop features, generate training data, validate it, and deploy to production. Engineers can prototype features and test them with real production data without creating pull requests or waiting on other teams.
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Instant Performance: Real-Time Feature Serving in Milliseconds
For Coinbase’s fraud detection system, speed is critical. When a user takes an action, such as attempting to withdraw funds, the system needs to fetch features, generate a fraud prediction score, and take appropriate action—all within milliseconds.
“This is something that happens instantaneously, and happens every time a user takes an action at Coinbase,” explains McAllister. Tecton delivers the required feature values with very low latency, which is essential for these real-time use cases.
Why Tecton is Essential
The impact of Tecton on Coinbase’s ML operations has been transformative. “Without Tecton, I don’t think we’re going to be able to get the most value out of machine learning. It would become a bottleneck around feature engineering,” McAllister states.
The platform has enabled Coinbase to:
- Run hundreds of feature pipelines concurrently
- Serve features in real-time for dozens of models
- Monitor pipeline health automatically
- Identify unused features that can be turned off
- Support diverse users from data scientists to analysts
Looking ahead, McAllister sees Tecton’s potential expanding: “Tecton already is very effective at transforming different kinds of data and generating datasets… There’s a lot that can be done around computing and serving embeddings, as well as getting other real-time context into models.”
By providing robust infrastructure for feature management, Tecton has become a robust addition to Coinbase’s ML operations, enabling them to focus on building features that drive business value.
Book a Demo
Engage with a product expert in a one-on-one session to explore what you can do with Tecton.
Try Tecton
Our interactive demo allows you to explore and try our AI Data Platform in just 5 minutes!
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