Building best-in-class recommendation systems with the TensorFlow ecosystem

Posted by Wei Wei, Developer Advocate

that gathers all the tooling and learning resources for creating recommendation systems, and provides a guided path for you to choose the right products to build with.

While it is relatively straightforward to follow the , modern large scale recommenders in production usually have strict latency requirements, and thus, are more sophisticated and require a lot more than just a single API or model. The generated recommendations from these recommenders are typically a result of a complex dance of many individual ML models and components seamlessly working together. Over the years Google has open sourced a suite of TensorFlow-based tools and frameworks, such as library, deploy with and . And if you want to experiment with more advanced models such as graph neural networks or reinforcement learning, we have listed additional libraries for you as well.

This unified page is now the entry point to building recommendation systems with TensorFlow and we will keep updating it as more tools and resources become available. We’d love to hear your feedback on this initiative, please don’t hesitate to reach out via the TensorFlow forum.