Denis’s Substack • 7 HN points • 07 Jun 23
- 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.