machine learning operations
[/məˈʃiːn ˈlɜːrnɪŋ ˌɑːpəˈreɪʃənz/]
nounpl: machine learning operations
operações de aprendizado de máquina
1. A set of practices and tools for managing, deploying, monitoring, and maintaining machine learning models in production environments
Machine learning operations ensures that our predictive models remain accurate and perform efficiently in real-world applications.
As operações de aprendizado de máquina garantem que nossos modelos preditivos permaneçam precisos e com desempenho eficiente em aplicações do mundo real.
2. The discipline that bridges the gap between data science and software engineering, focusing on the lifecycle of ML models from development to production
Our team implemented machine learning operations to automate model deployment and reduce time-to-market.
Nossa equipe implementou operações de aprendizado de máquina para automatizar a implantação de modelos e reduzir o tempo de colocação no mercado.
3. Practices including model versioning, continuous integration/continuous deployment (CI/CD), monitoring, and governance for ML systems
Machine learning operations includes tracking model performance metrics and managing data quality throughout the model lifecycle.
As operações de aprendizado de máquina incluem rastreamento de métricas de desempenho do modelo e gerenciamento da qualidade dos dados durante todo o ciclo de vida do modelo.
Machine learning operations (MLOps) emerged as a critical discipline in the late 2010s as companies struggled to move machine learning models from research to production. In Brazil and Portugal, the term is increasingly adopted in tech companies, particularly in São Paulo's growing tech ecosystem and Lisbon's startup scene. The abbreviation 'MLOps' is commonly used globally, including in Portuguese-speaking regions, reflecting the technical nature of the field.
Related Idioms & Phrases
putting models into production
managing the model lifecycle
bridging the gap between data science and engineering
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