As machine learning (ML) and artificial intelligence (AI) become core to decision-making in critical sectors like finance, healthcare, and logistics, ensuring that models perform reliably over time is no longer optional—it’s essential. Left unchecked, model performance can degrade due to data drift, concept drift, or operational failures, leading to inaccurate outputs, customer dissatisfaction, or even regulatory breaches.
More than ever before, people around the world are impacted by the advancement in AI. AI is becoming ubiquitous and it can be seen in healthcare, retail, finance, government, and practically anywhere imaginable. We use it to improve our lives in many ways such as automating our driving, detecting diseases more accurately, improving our understanding of the world, and even creating art. Lately, AI is becoming even more available and “democratized” with the rise of accessible generative AI such as ChatGPT.
In recent years, the term MLOps has become a buzzword in the world of AI, often discussed in the context of tools and technology. However, while much attention is given to the technical aspects of MLOps, what's often overlooked is the importance of the operations. There is often a lack of discussion around the operations needed for machine learning (ML) in production, and monitoring specifically. Things like accountability for AI performance, timely alerts for relevant stakeholders, the establishment of necessary processes to resolve issues, are often disregarded for discussions about specific tools and tech stacks.
Monitoring is crucial to ensuring that ML models deployed in production are serving their intended purpose and operating as expected, but how soon is too soon to implement a monitoring solution? Ultimately it depends on the extent to which the models are integrated into business processes, and this blog post will walk through the considerations that should be made before deciding to implement an ML monitoring solution.