Much of the hardest work of creating effective data products in the enterprise is not in the complexity of the algorithms applied but in effective design and integration into downstream systems. Hilary Mason shares a process for repeatedly creating effective AI products, from idea through process to specific design considerations, and explains how architecture and algorithmic choices can support or hinder this process.
Hilary Mason is the general manager for machine learning at Cloudera. Previously, she founded Fast Forward Labs, an applied machine learning research and advisory company (acquired by Cloudera in 2017). Hilary is the data scientist in residence at Accel Partners and serves on the board of the Anita Borg Institute. Formerly, she cofounded HackNY.org, a nonprofit that helps engineering students find opportunities in New York’s creative technical economy, served on Mayor Bloomberg’s Technology Advisory Council, and was the chief scientist at Bitly. Hilary can be reached on Twitter @hmason and on LinkedIn.
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