Wenjie Zi, a senior machine learning engineer at Grammarly, recently discussed the reasons why many ML projects fail in the real world in a podcast with Srini Penchikala. Zi highlighted five common pitfalls that lead to the failure of ML projects: starting with the wrong problem, data challenges, difficulties in turning models into products, discrepancies between offline success and online performance, and non-technical blockers.
She emphasized the importance of collaboration between business leadership and ML practitioners to address these challenges. Zi shared her experience working with personal banking on credit projects and how deep dive sessions helped bridge the communication gap between domain experts and AI engineers.
Regarding emerging trends, Zi discussed the rise of agentic AI systems and the challenges of generative AI and large language models. She advised companies to invest time in evaluating the success of their gen AI solutions to stand out from competitors.
In terms of tools and resources, Zi recommended online materials, books, and attending tech talks to stay updated on AI advancements. She also highlighted the Toronto AI Practitioners Network as a valuable community resource for AI professionals.
Overall, the discussion provided valuable insights into the complexities of deploying ML applications in production and the importance of addressing both technical and non-technical aspects for project success. Read more on the AI/ML and Data engineering community page on InfoQ for further insights and resources.
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