In recent years, Machine Learning (ML) has propelled software systems into new realms of capability. From revolutionizing medical assistance and personalized recommendations to enabling chatbots and self-driving cars, ML has become a cornerstone of modern technology.

Despite these advancements, the path from an ML concept to a fully operational product is riddled with obstacles. Crafting an accurate model is challenging enough, but productionising it into a successful, robust product requires more than just a well-trained algorithm. The more critical aspects of building a scalable infrastructure, ensuring continuous monitoring, automated retraining, and data wrangling are often overlooked.