Many machine learning projects never make it to production due to various reasons like lack of stakeholder buy-in and data quality issues.
The traditional linear process of analyzing, extracting data, modeling, deploying, and operating models can be naive and not reduce uncertainty.
Embracing uncertainty in machine learning deployments can involve starting the deployment phase before data extraction, leading to constant value addition throughout the process.