Machine Learning in Production: Lessons from 100 Enterprise Deployments

<p>After helping over 100 enterprises deploy machine learning systems into production, we have identified patterns that separate successful deployments from those that stall. Here are the most important lessons we have learned.</p><h2>Data Quality Trumps Model Complexity</h2><p>The most sophisticated model cannot overcome poor data quality. Invest in data infrastructure before model development.</p><h2>Start Small, Scale Fast</h2><p>Begin with a focused proof of concept that demonstrates clear business value, then scale systematically.</p><h2>MLOps is Not Optional</h2><p>Model monitoring, versioning, and automated retraining pipelines are essential from day one, not afterthoughts.</p><h2>Cross-Functional Teams Win</h2><p>Successful ML projects require collaboration between data scientists, engineers, domain experts, and business stakeholders.</p>