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