Wenjie Zi, a Senior Machine Learning Engineer at Grammarly and co-founder of the Toronto AI practitioners network, shares her insights on the hurdles many ML projects face. She discusses the critical gaps in communication between business teams and ML practitioners and how to bridge them. Wenjie highlights common pitfalls like data quality issues and the importance of setting realistic expectations. She also dives into the influence of emerging generative AI systems, stressing the value of community engagement in the rapidly evolving AI landscape.
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volunteer_activism ADVICE
Choose the Right Problems
Start by tackling the right problem, ensuring an ML solution is actually necessary.
A simpler, rule-based solution might suffice, or the company may not be AI-ready.
insights INSIGHT
Data Quality is Crucial
In ML, 'garbage in, garbage out' highlights the criticality of data quality.
Inconsistent formats, mislabels, and biased sampling can silently cause project failure.
question_answer ANECDOTE
Offline vs. Online Success
Offline success doesn't guarantee online success, as seen in an image recommendation project.
Data sampling and differing evaluation metrics between offline and online caused misalignment.
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In this podcast, Wenjie Zi discusses why many ML projects don’t succeed and what technology and organizational aspects affect the success of those projects. She also talked about what potential communication and understanding gaps can exist between business team and ML practitioners and how to address these gaps.
Read a transcript of this interview: https://bit.ly/4lwbGsk
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