Yotpo: Leading eCommerce retention marketing platform enhanced with AI
Initially contemplating the development of their own MLOps system, Yotpo's meticulous research shifted their strategy. Recognizing the importance of focusing on their core expertise, they opted to integrate with Qwak — a robust MLOps platform that not only aligns with their current needs but also offers the adaptability to sustain their envisioned growth trajectory.
Yotpo is an eCommerce marketing platform that helps brands drive growth by creating engaging experiences to build lasting customer relationships. Yotpo's integrated solutions for reviews, visual User Generated Content (UGC), rewards, and referrals empower businesses to win over new audiences using their customers' voice.
Industry
Ecommerce Marketing, Social Media, Adtech
Use Case
Model Frameworks
Qwak is built in a flexible way. Out of the box functionality is great but we were really glad to see that the platform was developed with flexibility and customizability in mind. That ensures that we can depend on it for the long term
End user recommendations
Engineering dependency
Platform Flexibility
Challenges
- Intensive Engineering Support: Crafting recommendation engine models demanded significant engineering backup.
- Infrastructure Scalability: The infrastructure needed to be robust enough to support billions of users concurrently.
- Platform Requirements: Yotpo sought a real-time ML model serving platform that was reliable, user-friendly, scalable, and forward-looking.
- Flexibility & Autonomy: It was pivotal that Yotpo remained unshackled by specific model structures. They also required a centralized mechanism to seamlessly train, deploy, and access models on a massive scale.
- Operational Overhead: Building and maintaining such infrastructure demanded extensive expertise, time, and effort—areas not aligned with Yotpo's primary business objectives.
Solutions
- Intensive Engineering Support: Crafting recommendation engine models demanded significant engineering backup.
- Infrastructure Scalability: The infrastructure needed to be robust enough to support billions of users concurrently.
- Platform Requirements: Yotpo sought a real-time ML model serving platform that was reliable, user-friendly, scalable, and forward-looking.
- Flexibility & Autonomy: It was pivotal that Yotpo remained unshackled by specific model structures. They also required a centralized mechanism to seamlessly train, deploy, and access models on a massive scale.
- Operational Overhead: Building and maintaining such infrastructure demanded extensive expertise, time, and effort—areas not aligned with Yotpo's primary business objectives.
