Machine learning models are becoming increasingly important in emerging products and technologies, transforming the process of building ML models from an art to an engineering practice. Ensuring a reliable experience for users is crucial when deploying models into production environments, where issues can have significant impacts on revenue and customer satisfaction. As the industry matures, reliability engineering has become essential to prevent model drift or failure, which can cause sudden and significant problems, such as plummeting sales and customer complaints. The importance of reliability engineering in ML initiatives cannot be overstated, requiring a structured approach to ensure successful model deployment and maintenance.