ML engineering

[ɛm ɛl ˈɛndʒɪnɪərɪŋ]
noun
engenharia de aprendizado de máquina
1. The practical application of machine learning techniques to design, build, deploy, and maintain machine learning systems in production environments
ML engineering involves data preprocessing, model selection, hyperparameter tuning, and continuous monitoring.
A engenharia de aprendizado de máquina envolve pré-processamento de dados, seleção de modelos, ajuste de hiperparâmetros e monitoramento contínuo.
2. The discipline that bridges machine learning research and software engineering practices to create reliable, scalable AI solutions
ML engineering requires expertise in both data science and software development best practices.
A engenharia de aprendizado de máquina requer expertise tanto em ciência de dados quanto nas melhores práticas de desenvolvimento de software.
ML engineering has become increasingly important in Brazil's growing tech sector, particularly in São Paulo's startup ecosystem. In the USA, especially in Silicon Valley and NYC tech hubs, ML engineering roles are in high demand and often command premium salaries. The term reflects the maturation of machine learning from academic research to practical, production-level implementation across industries.
NYC Slang
ML eng (abbreviated in tech industry conversations)
Synonyms / Sinônimos
machine learning engineeringML opsMLOpsapplied machine learningAI engineering
Antonyms / Antônimos
machine learning researchtheoretical AI

Regional Variations

General Brazilian Portuguese
engenharia de aprendizado de máquina
Standard technical term used in academia and industry
São Paulo
engenharia de ML
Abbreviated form commonly used in tech hubs and startup environments
Rio de Janeiro
engenharia de aprendizado de máquina
Formal usage in academic and corporate contexts
Portugal
engenharia de aprendizagem automática
Uses 'aprendizagem automática' instead of 'aprendizado de máquina'

Related Words

machine learningdata sciencedeep learningartificial intelligencemodel deploymentdata pipelinefeature engineeringmodel validation

Related Idioms & Phrases

putting models into production
scaling machine learning systems
closing the loop on ML pipelines
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