In most technology companies, machine learning is limited to products built by a handful of teams with specific ML products, such as search, notifications, and content ranking. These teams often have a specific ML pipeline for their product, with minimal flexibility in adding and removing features and changing the models.
Development for new machine learning-based products can take an extremely long time. As a result, larger companies such as Uber and Apple have recently built flexible machine learning pipelines that enable fast experimentation and deployment of new ML products by any engineering team throughout the company. Essentially they are democratizing machine learning.
Dorna Bandari offers an overview of the machine learning pipeline at B2B AI startup Jetlore and explains why even small B2B startups in AI should invest in a flexible machine learning pipeline. Dorna covers the design choices, the trade-offs made when implementing and maintaining the pipeline, and how it has accelerated Jetlore’s product development and growth.
Dorna Bandari is the director of algorithms at AI-driven prediction platform Jetlore, where she leads development of large-scale machine learning models and machine learning infrastructure. Previously, she was a lead data scientist at Pinterest and the founder of ML startup Penda. Dorna holds a PhD in electrical engineering from UCLA.
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