DQDF: Data-Quality-Aware Dataframes
Phanwadee Sinthong, Dhaval Patel, et al.
VLDB 2022
Forecasting is a key AI component that drives various supply chain use cases such as inventory management, markdown optimization, etc. In general, supply chain use cases deal with large-scale data that needs sophisticated distributed forecasting techniques. These techniques involve a lot of complex steps such as pipeline construction, set-up/execution across multiple distributed environments (ray, spark), HPO, right model selection, backtesting, evaluation, etc. Manually coding and orchestrating these tasks is highly time-consuming and error-prone. To tackle this, we propose our in-house built YAML-driven orchestration engine that automates and eases various complex distributed forecasting tasks for faster supply chain decisions.
Phanwadee Sinthong, Dhaval Patel, et al.
VLDB 2022
Tyler Baldwin, Wyatt Clarke, et al.
Big Data 2022
Amit Alfassy, Assaf Arbelle, et al.
NeurIPS 2022
Ademir Ferreira Da Silva, Levente Klein, et al.
INFORMS 2022