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We take a look back at what we say on infoQ in 2018, and think about what the next year might bring.
Matthew Parker attempts to dispel the myth that architecture does not need to be agile.
Ville Tuulos talks about choices and reasoning the Netflix Machine Learning Infrastructure team made on developing tooling for the data scientists and some of the challenges and solutions made to create a paved road for machine learning models to production.
Lisa Doan, Jeanette Head, Hadrien Raffalli discuss the conflict between production teams who prefer an agile process and stakeholders who want cleat schedules and outcome.
The fact that machine learning development focuses on hyperparameter tuning and data pipelines does not mean that we need to reinvent the wheel or look for a completely new way. According to Thiago de Faria, DevOps lays a strong foundation: culture change to support experimentation, continuous evaluation, sharing, abstraction layers, observability, and working in products and services.
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