Observability and monitoring is the most cited challenge when moving ML models into production. The Institute for Ethical AI & Machine Learning conducted a survey on the state of production ML in the fourth quarter of 2024. The other key takeaway is that custom-built tools dominate user roadmaps, since few vendor tools have gained significant traction.
Only 7% say that ML security is one of their top three challenges and only 17% say the same about governance and domain risks. That finding is significantly different from what we’ve seen in other studies, where security and AI governance are cited as among the biggest obstacles to increased adoption. We believe the practitioners view ML security as pertaining just to the ability of a model to be hacked, while other IT decision-makers worry more about general access to corporate and personal data.
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