Learn about the challenges and solutions for scaling machine learning (ML) models to production, as highlighted in this informative document. Despite the low success rates reported by Gartner and McKinsey, it emphasizes that technical expertise alone is not enough for successful ML projects. Understanding the alignment between business needs and ML objectives is crucial. The article addresses common issues like maintaining model accuracy and data quality over time, stressing the importance of a robust development process. GAP provides valuable insights into overcoming these challenges through practices such as AutoML and MLOps, enhancing the scalability and efficiency of ML deployments.