Machine Learning (ML) Engineering DevOps Practice

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A Machine Learning (ML) Engineering DevOps Practice is a DevOps practice for ML Engineers.



References

2022

  • https://cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning
    • QUOTE: This document is for data scientists and ML engineers who want to apply DevOps principles to ML systems (MLOps). MLOps is an ML engineering culture and practice that aims at unifying ML system development (Dev) and ML system operation (Ops). Practicing MLOps means that you advocate for automation and monitoring at all steps of ML system construction, including integration, testing, releasing, deployment and infrastructure management.

      Data scientists can implement and train an ML model with predictive performance on an offline holdout dataset, given relevant training data for their use case. However, the real challenge isn't building an ML model, the challenge is building an integrated ML system and to continuously operate it in production. With the long history of production ML services at Google, we've learned that there can be many pitfalls in operating ML-based systems in production. Some of these pitfalls are summarized in Machine Learning: The high-interest credit card of technical debt.

      As shown in the following diagram, only a small fraction of a real-world ML system is composed of the ML code. The required surrounding elements are vast and complex.