The InfoQ Podcast
Wenjie Zi on Technology and Organizational Aspects for ML Project Success
Apr 16, 2025
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.
22:13
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Quick takeaways
- Success in machine learning projects is often undermined by misalignment between business stakeholders and ML practitioners regarding project goals and implementation requirements.
- The rapid development potential of generative AI must be balanced with quality assurance, highlighting the importance of iterative feedback and domain-specific data.
Deep dives
Common Reasons for ML Project Failures
Many machine learning projects fail due to several common pitfalls that teams should be aware of. One major issue is tackling the wrong problem, where teams mistakenly believe they need ML solutions for challenges that can be addressed with simpler, rule-based approaches. Data quality also presents a significant challenge, as poor or messy datasets can lead to ineffective models. Additionally, the complexity of turning models into fully functional products often catches teams off guard, resulting in misalignment between offline testing success and online performance.
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