With its unparalleled ability to turn data from raw material into a valuable resource, AI is more than an opportunity to innovate. Nevertheless, this doesn’t make it the answer to every problem that involves data. Although understanding and avoiding these misconceptions is essential, there is another equally critical consideration when it comes to realising AI's impact. Namely, the question of how to turn models that are theoretically performant pre-deployment into operationalised systems in the real world.
Recent posts by Alistair Garfoot
5 min read
As AI capabilities advance, and with the advent of widely available low-cost cloud computing, AI will inevitably be applied to wider problem sets with more immediate and wide-ranging real-world impacts, bringing with it higher problem complexity and increased risk. As problem complexity and risks grow, the assurance of AI performance data cleanliness naturally transitions from pre-deployment considerations, instead becoming continuously evolving requirements which must be monitored and iterated on throughout a model’s life.