Pinterest's recent integration of Ray into its machine learning infrastructure faced unique challenges, requiring creative solutions to enhance capabilities while adhering to existing operational constraints.
To navigate the limitations of their Kubernetes cluster, Pinterest developed a custom API Gateway, a Ray Cluster Controller, and a dedicated UI for logging and metrics.
The implementation of Ray was driven by Pinterest's need for advanced machine learning solutions to tackle essential business problems, ensuring efficient usage of resources.
In managing User Experience, Pinterest's new setup allows for log analysis without an active Ray cluster, optimizing resource costs and integrating tightly with their proprietary databases.
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