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Like any large tech company, data is the backbone of the Uber platform. Not surprisingly, dataquality and drifting is incredibly important. Many datadrift error translates into poor performance of ML models which are not detected until the models have ran.
Axfood has a structure with multiple decentralized data science teams with different areas of responsibility. Together with a central dataplatform team, the data science teams bring innovation and digital transformation through AI and ML solutions to the organization. Workflow B corresponds to model qualitydrift checks.
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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