This article discusses the essential ML practices and platforms used by data science teams to collaborate on model development, configure infrastructure, deploy ML models, and maintain models at scale. It also explains the difficulty of explaining these practices and tools to business stakeholders and budget decision-makers, who need to understand the return on investment and business impact of machine learning and artificial intelligence.