Srini Penchikala, lead Editor for AI/ML and data engineering at InfoQ, recently interviewed Wenjie Zi, senior machine learning engineer at Grammarly, on why many ML projects fail in the real world. Wenjie shared her insights on the common pitfalls that lead to project failures, emphasizing the importance of tackling the right problems, data quality, transitioning models into products, online performance evaluation, and non-technical blockers.
She also highlighted the challenges of aligning business leadership with ML practitioners and stressed the need for clear communication and understanding between the two groups. Wenjie discussed the rise of AI agents in the industry and cautioned against the hype surrounding generative AI, urging companies to invest in domain-specific data and evaluation processes.
In terms of technology and tools, Wenjie recommended online resources, books like Chip Huyen’s publications, and community engagements like the Toronto AI Practitioners Network. She also shared her experiences with agent technology and its applications in sales and customer service.
The interview concluded with a discussion on the evolving trends in AI and the importance of successful deployment of ML applications in production. Listeners were encouraged to explore the AI/ML and Data engineering community page on InfoQ for more articles and podcasts on AI topics.
For more information and resources mentioned in the interview, including the Google Whitepaper on Hidden Technical Debt in ML Systems and Chip Huyen’s books, listeners can refer to the show notes.
————————————————————————
Note: The image is for illustrative purposes only and is not the original image associated with the presented article. Due to copyright reasons, we are unable to use the original images. However, you can still enjoy the accurate and up-to-date content and information provided.
————————————————————————
