The goal of any machine learning (ML) project is to produce high-quality models quickly, but in reality, each ML project takes months from identifying the problem and use case to deploying the model in production. High-quality data is crucial for building high-quality models, as it's the most significant impediment to seamless ML adoption across the enterprise. To build a platform that helps curate high-quality models through high-quality datasets, it's essential to understand how your data is distributed, including its semantic coverage, outliers, noise, and semantically confusing features. A good machine learning platform should be able to evaluate a model on a hybrid set of metrics, including prediction latency, feature importance, and class balance. The system should also enable users to monitor and observe custom combinations of key metrics and provide actionable steps to fix issues automatically. By focusing on quality over quantity and using techniques like active learning and pre-trained embeddings, developers can build high-quality models that meet the real-world demands of their business.