ML as Code arrives: production ML with SageMaker, Databricks, Azure ML Studio and Hugging Face

AI Impact Summary

The article frames ML as Code as a maturation of ML practice, advocating production-focused MLOps, infrastructure-as-code, and cloud-native tooling to move models from sandbox to production. It highlights the growing reliance on transformer models across domains and the Hugging Face ecosystem (including AutoNLP) as accelerants for real-world deployment. For engineering teams, this signals a need to standardize versioning, monitoring, and governance while leveraging platforms like SageMaker, Databricks, and Azure ML Studio to scale ML pipelines cost-effectively.