How to Gauge Project Feasibility with Rapid Prototyping using Code-First Data

Presented by

Mel Hanna, Senior Data Scientist | Joseph Blue, Customer Facing Data Scientist

About this talk

Data scientists' time is valuable. Computing resources are expensive. With only 87% of projects ever making it to production (Source: VentureBeat), organizations often overcommit to costly projects that bear little fruit. Data science teams need a way to assess project feasibility without diving head first. Join us to learn how you can create a rapid baseline assessment to determine project feasibility using numerical, categorical, and image data. Help your team prioritize work and allocate resources accordingly. You will learn how to: Prioritize research, assess multi-modal data sources and optimize DS time Use rapid prototyping for quick feasibility assessment Incorporate output from multiple images into a single prediction
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