Background

Embeddings are a critical component of machine learning models, but managing them can be challenging for developers without ML expertise. Our team recognized this gap in the market and set out to create Embedbase, an open-source suite that simplifies the process of managing embeddings for app developers.

Problem

App developers with little ML experience often struggle to manage embeddings, which can lead to suboptimal performance and wasted time. Existing solutions were either too complex or too expensive for these developers, leaving a gap in the market for a more accessible option.

Solution

We created Embedbase, an open-source suite that makes it easy for app developers to manage embeddings. With Embedbase, developers can quickly create recommendation engines or classification engines without needing extensive ML knowledge. The suite includes pre-trained embeddings and tools for training custom embeddings, making it a versatile solution for a range of use cases.

Impact

Increased accessibility of machine learning technology for app developers
Simplified management of embeddings leading to improved model performance
Potentially reduced development time and costs by providing an open-source solution
Promoted collaboration within the developer community through open-source contributions and feedback

Team

Ben Shafii - co-founder
- Oversaw project planning and execution, managed team communication and timelines
Louis - co-founder
- Built pre-trained embedding models and developed tools for custom embedding training

Open-Source Embedding Suite for App Developers