Data Prep: What Data Scientists Wish You Knew
If you’re considering a machine learning project, you probably know that you need data, and lots of it. And while many companies are swimming in volumes of data, that data is almost never ready for AI and ML projects. It must be prepared, which can include cleansing, annotation, and more. CloudFactory’s VP of Client Success Paul Christianson and Infinia ML data scientist Ben Schneller discuss what data scientists wish you knew about preparing your data for AI projects. Their conversation covered topics such as: How much time your AI project should allocate to data prep and annotation The importance of having a “data readiness” strategy Operationalizing data prep and annotation to produce high quality training data at scale
Session ID: Presentation Type: On-Demand Session (Recorded)
Date / Time: [Content On-Demand] @ On Demand ET (US)
Presented by:CloudFactory is a global leader in combining people and technology to provide a workforce in the cloud for machine learning and core business data processing. Click for more details and additional sessions and content.
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