3 differences between ML in production and in academia

Machine learning (ML) in production significantly differs from its academic counterpart, primarily due to the multifaceted nature of real-world applications that extend beyond solely optimizing model performance. In production, ML systems must function as cohesive entities involving various stakeholders with sometimes conflicting objectives, such as balancing model complexity, inference speed, and infrastructure readiness. Interpretability becomes crucial, as users need to trust and understand the models' predictions, which is often sidelined in academic settings focused on predictive accuracy. Fairness and bias also take on heightened importance since deploying biased models can lead to negative social impacts and reputational damage, emphasizing the need for ethical considerations in ML deployment. These elements illustrate the complexities of ML systems in production, highlighting the necessity for practitioners to integrate performance, transparency, and ethics into their workflows to create effective and responsible ML solutions.