5 things you should know about AutoML

5 Considerations for AutoML

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AutoML platforms and solutions are quickly becoming the dominant way for every organization that is looking to implement and scale their ML and AI projects. As Forrester pointed out, these tools are trying to automate the end-to-end life cycle of developing and deploying predictive models — from data prep through feature engineering, model training, validation and deployment. 

This often involves evaluating numerous platforms and identifying the best fit for your organization. The decision process is based on multiple considerations, including accuracy, ease-of-use, performance, integration with existing tools, economics, competitive differentiation, solution maturity, risk tolerance, regulatory compliance considerations and more.

Watch this webinar to listen to H2O.ai's Kaggle Grandmaster, Bojan Tunguz and Vinod Iyengar, VP Marketing and Technical Alliances discuss the top 5 considerations in selecting an AutoML platform.

Vendor:
H20.ai
Premiered:
Nov 22, 2019
Format:
PDF
Type:
Webcast
Language:
English
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