# The Age of Machine Learning As Code Has Arrived

huggingface.co · Julien Simon · Oct 20, 2021

**Type:** Explainer

## Summary

Recent findings from the 2021 State of AI Report and Kaggle State of Machine Learning and Data Science Survey indicate that machine learning is expanding into critical infrastructure while Transformers become general-purpose architectures across text, vision, and audio. In response, organizations face questions about scaling infrastructure, team composition, and engineering maturity. Rather than treating machine learning as isolated sandbox experiments or hiring solely data scientists, teams benefit from adopting established software engineering and DevOps principles like versioning, testing, automation, and continuous deployment. Furthermore, the rise of pre-trained Transformer architectures enables practitioners to fine-tune existing off-the-shelf models rather than training from scratch, reducing compute costs and training duration. Tools from platforms such as Hugging Face streamline model deployment, latency optimization, and infrastructure abstraction.

## Context

The 2021 State of AI Report and Kaggle survey indicate rapid growth of machine learning in mission-critical infrastructure, raising questions about whether industry maturity and engineering workflows can keep pace.

## Approach / What changed

Apply proven software engineering and DevOps practices—such as automation, testing, versioning, and cloud infrastructure abstraction—while adopting pre-trained Transformer models and off-the-shelf tooling from Hugging Face instead of training deep learning models from scratch.

## Takeaways

- According to the Kaggle survey, 75% of respondents use cloud services and over 45% use enterprise ML platforms, led by Amazon SageMaker, Databricks, and Azure ML Studio.
- Transformers are expanding beyond NLP into computer vision, audio, and point clouds, setting benchmarks with models like Vision Transformer and CoAtNet while requiring fewer training compute resources.
- Decade-old software engineering practices—including versioning, reusability, testability, automation, and monitoring—must be applied to machine learning systems to move beyond sandbox experiments into production.

**Tags:** [Architecture](https://yomu.fyi/topic/architecture), [CI/CD](https://yomu.fyi/topic/ci-cd), [Deployment](https://yomu.fyi/topic/deployment), [Developer Experience](https://yomu.fyi/topic/developer-experience), [Machine Learning](https://yomu.fyi/topic/machine-learning)

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