Session
10 Things That Can Go Wrong w/ML Projects (and what you can do about it)
Machine learning practitioners are solving important problems every day. They're also experiencing a new set of challenges that are unique to ML projects.
This session will cover what to watch out for in terms of building a model; model accuracy; transparency and fairness; and MLOps.
The good news is that there are solutions. Attendees will hear about best practices and tools that will help address these issues.
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