About Feeld
Feeld is a dating app for the curious; for those open to experiencing connection and relationships in a new way. The UK-founded startup’s mission is to elevate the human experience of sexuality and relationships and create a world where everyone is more intimately connected to each other and themselves.
Feeld’s ever-evolving platform creates more inclusive spaces online and IRL, where everyone can feel safe to express and explore gender, sexuality, and desire outside of existing blueprints. It actively build trust with policies and practices that evolve along with our community.
The Challenge: Scaling Infrastructure to Protect the Feeld Community
To maintain an authentic and safe environment, Feeld invests heavily in building advanced technological systems. However, as the platform expanded, the engineering team faced a critical operational hurdle:
- Fraud detection: Identifying and preventing spam, fake accounts, and fraudulent activity on the platform became a primary challenge.
- Unsuitable infrastructure: It’s initial artificial intelligence setup relied on a scheduled notebook workflow. This underlying infrastructure became increasingly difficult to maintain and scale, and presenting a challenge to building a robust, scalable infrastructure.
- Relying on ad hoc processes created manual bottlenecks: These bottlenecks made it tough to efficiently retrain, update, or deploy new machine learning models.
- Lack of flexible inference capabilities: Because of this, Feeld was forced to depend on rigid, always-on infrastructure that could not scale dynamically based on different use cases.
- Misaligned engineering capacity: Developers were losing valuable time building infrastructure from scratch for every new model instead of solving core product problems.
Feeld also wanted to transition away from a third-party fraud detection vendor to reduce costs without sacrificing security.
The Solution: Automated MLOps on AWS
Feeld needed a strategic partner with deep expertise in machine learning and the technical knowledge required to architect scalable production systems within the Amazon Web Services (AWS) ecosystem. They selected Automat-it because of the team’s AWS AI Services Competency and proven ability to design robust infrastructure using AWS machine learning services.
Working closely with Feeld, Automat-it overhauled the legacy setup and implemented a highly structured, automated machine learning framework. Key architectural improvements included:
- End-to-End Lifecycle Management: The legacy notebook setup was replaced with standardized, automated workflows. This makes it frictionless to efficiently retrain, update, and deploy models as the product evolves.
- Serverless Inference Scaling: Automat-it introduced flexible inference deployment options using AWS Lambda. This enables the infrastructure to scale up or down efficiently based on specific workload demands without relying on always-on compute resources.
- Unified Cloud Architecture: The engineering team designed a fully Terraform-managed, multi-account AWS environment tailored for complex AI workloads. This centralized platform decoupled infrastructure management from data science.
- Adaptive Learning: The team implemented daily automated retraining cycles. These cycles help the system stay ahead of evolving spam tactics and fraud patterns.
The Results: Faster Iteration and Reliable Performance
By moving to a structured MLOps pipeline, Feeld bypassed common architectural pitfalls and transformed its machine learning capabilities from day one.
Key outcomes from this partnership include:
- Enhanced Platform Safety: The new fraud detection process successfully reduced fake profiles. This resulted in a much safer and more secure user experience for the community.
- Accelerated Speed to Market: Standardized pipelines drastically improved the speed at which developers can iterate and experiment. Critical new models, such as core architecture and recommendation systems, now reach production much faster than before.
- Improved Reliability: Moving to a structured ML framework significantly boosted the overall stability and observability of Feeld’s backend systems.
- Reduced Operational Overhead: Once models are deployed through the new pipeline, they require far less operational maintenance. This operational efficiency frees up the internal team’s time. They can now focus entirely on launching new initiatives and delivering real product value instead of fixing infrastructure issues.
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