At Pallet, Connie has been leading various data science work streams on integrating machine learning, intelligence, and automation into the entire Pallet product--including creating multi-label, multi-class auto labeling machine learning models deployed as cloud-native APIs to be integrated into Pallet backend, creating holistic and diagnosable health scores for various stakeholders and scaling out Pallet's data collection system.
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At Pallet, Connie has been leading various data science work streams on integrating machine learning, intelligence, and automation into the entire Pallet product--including creating multi-label, multi-class auto labeling machine learning models deployed as cloud-native APIs to be integrated into Pallet backend, creating holistic and diagnosable health scores for various stakeholders and scaling out Pallet's data collection system.
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SUMMARY
How do we effectively integrate and utilize data science in mature engineering and business systems?
Connie talks about her experience in how this can be done, drawing from both experiences in Big Tech and leading the data efforts at an early-stage tech startup.
