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How Tripadvisor Delivers Real-Time Personalization at Scale with ML
This story was originally published on HackerNoon at: https://hackernoon.com/how-tripadvisor-delivers-real-time-personalization-at-scale-with-ml.
Tripadvisor uses ML and ScyllaDB on AWS to deliver real-time personalization at massive scale with millisecond latency and advanced data architecture.
Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning.
You can also check exclusive content about #tripadvisor-personalization, #real-time-ml, #scylladb-aws, #machine-learning-architecture, #ml-feature-store, #cassandra-migration, #kubernetes-microservices, #good-company, and more.
This story was written by: @scylladb. Learn more about this writer by checking @scylladb's about page,
and for more stories, please visit hackernoon.com.
Tripadvisor delivers real-time personalization to over 400M monthly users using ML models powered by ScyllaDB on AWS. Their Visitor Platform processes billions of daily requests with millisecond latency, leveraging a custom feature store, Kubernetes microservices, and data pipelines. Migrating from Cassandra to ScyllaDB boosted performance and reduced operational overhead.
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By HackerNoonThis story was originally published on HackerNoon at: https://hackernoon.com/how-tripadvisor-delivers-real-time-personalization-at-scale-with-ml.
Tripadvisor uses ML and ScyllaDB on AWS to deliver real-time personalization at massive scale with millisecond latency and advanced data architecture.
Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning.
You can also check exclusive content about #tripadvisor-personalization, #real-time-ml, #scylladb-aws, #machine-learning-architecture, #ml-feature-store, #cassandra-migration, #kubernetes-microservices, #good-company, and more.
This story was written by: @scylladb. Learn more about this writer by checking @scylladb's about page,
and for more stories, please visit hackernoon.com.
Tripadvisor delivers real-time personalization to over 400M monthly users using ML models powered by ScyllaDB on AWS. Their Visitor Platform processes billions of daily requests with millisecond latency, leveraging a custom feature store, Kubernetes microservices, and data pipelines. Migrating from Cassandra to ScyllaDB boosted performance and reduced operational overhead.