From Chaos to Standards: The Ongoing Challenges of Machine Learning Engineering
Despite spectacular advances in artificial intelligence models, deploying a machine learning (ML) system remains, in many companies, a manual, unstable, and difficult-to-replicate process. In the absence of a shared methodology, AI projects struggle to move beyond the prototype stage due to code that is difficult to maintain, a lack of rigorous testing, or incomplete documentation.
Drawing on its extensive experience in deploying AI at scale, Google offers a methodological approach to this challenge through the MLE-STAR framework. Designed as a synthesis of best practices in software engineering tailored to machine learning, this framework aims to structure AI projects in a more reliable, modular, and sustainable way.
MLE-STAR: a methodological framework inspired by best practices in software development
Introduced by Google Research engineers in 2025, MLE-STAR is an acronym that refers to four fundamental stages in the development cycle of a machine learning system:
- Scoping
- Testing
- Abstracting
- Reuse
This framework is designed to guide ML engineers in designing robust systems, from initial scoping through to production deployment. MLE-STAR is based on a philosophy of responsible industrialization, in which each component of the pipeline is designed as a testable, reusable, and well-documented software building block.
A Closer Look at the 4 Pillars of MLE-STAR
Each dimension of MLE-STAR corresponds to a key practice in modern engineering as applied to machine learning:
- Scoping: defining the project’s objectives early on, along with expected performance metrics, technical constraints, and ethical boundaries. This phase helps prevent common pitfalls associated with poorly defined or overly vague objectives.
- Testing: Incorporate systematic testing at all levels of the code (unit tests, integration tests, model robustness tests). This includes verifying how the model behaves when faced with unexpected or noisy data.
- Abstracting: structuring the code in a modular way by separating business logic, ML components, and processing pipelines. This abstraction promotes maintainability, collaborative work, and system evolution.
- Reuse: Design reusable modules (preprocessing, evaluation, monitoring) that can be shared across projects or teams. This helps reduce code duplication and build on work that has already been done.
Tangible benefits for AI teams
According to Google’s teams, the systematic application of MLE-STAR is said to have enabled:
- a 40% reduction in the average time required to move from prototype to production in certain internal projects1
- a significant reduction in the rate of critical errors detected in production, thanks to improved test coverage
- faster onboarding of new engineers, made possible by a clearer, more modular code structure
MLE-STAR also fosters collaboration between data scientists, MLOps engineers, and product teams by establishing a common language grounded in technical rigor.
Limitations and conditions of implementation
Like any methodological framework, MLE-STAR requires a certain level of maturity to be effective. In particular, it requires:
- a well-structured organization with a well-established engineering culture
- the ability to train teams in these new practices
- internal tools (CI/CD, testing, versioning) tailored for ML
In exploratory or academic settings, rigid application of the framework could hinder the agility required for innovation. MLE-STAR is therefore better suited to industrial environments or large-scale ML projects.
Better Structure for Better Supervision: An Ethical Lever
Beyond engineering, MLE-STAR contributes to more responsible AI. By structuring projects from the outset, this framework facilitates:
- the traceability of decisions made (datasets, metrics, thresholds)
- the inclusion of tests specifically designed to assess equity or detect bias
- monitoring for drift or performance degradation over time
This approach allows for better documentation of the model’s behavior and helps anticipate the risks associated with its generalization. In the context of the European AI Act, this type of methodology could prove useful for demonstrating the compliance of systems deployed in high-risk environments.
Toward a standard for scalable ML engineering?
Google does not seek to impose a closed standard with MLE-STAR, but rather to foster a culture of rigorous engineering within the machine learning community. The framework can inspire other stakeholders, both in industry and academia.
Ultimately, we can envision MLE-STAR being integrated into AI training programs, open-source environments (TensorFlow, PyTorch Lightning), or even industry-specific best-practice guides. The widespread adoption of AI also depends on the standardization of business processes, tools, and methods.
Learn more
You can also read the article Artificial Intelligence Enters the Industrial Phase: Red Hat Unveils Its Open-Source Inference Server, which examines how Red Hat is standardizing AI inference in MLOps processes, a challenge complementary to that of ML engineering
References
1. Google Research. (2025). MLE-STAR: Structuring Machine Learning Engineering at Scale.
