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Like any large tech company, data is the backbone of the Uber platform. Not surprisingly, data quality and drifting is incredibly important. Many datadrift error translates into poor performance of ML models which are not detected until the models have ran.
The ML platform can utilize historic customer engagement data, also called “clickstream data”, and transform it into features essential for the success of the search platform. We can collect and use user-product historical interaction data to train recommendation system algorithms.
Tools range from dataplatforms to vector databases, embedding providers, fine-tuning platforms, prompt engineering, evaluation tools, orchestration frameworks, observability platforms, and LLM API gateways. Monitoring Monitor model performance for datadrift and model degradation, often using automated monitoring tools.
Stefan is a software engineer, data scientist, and has been doing work as an ML engineer. He also ran the dataplatform in his previous company and is also co-creator of open-source framework, Hamilton. As you’ve been running the ML dataplatform team, how do you do that? Datadrift.
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