MLOps
The practices and tools for deploying, monitoring, and maintaining ML models in production — bridging data science and software engineering.
MLOps applies DevOps principles to machine learning: versioning data and models, automating training pipelines, monitoring model drift, and managing the full model lifecycle from experiment to production. Tools like MLflow, Weights & Biases, and SageMaker are common. MLOps roles require understanding both the ML workflow and production engineering concerns — a combination that is genuinely rare and well compensated.
Typical time to job-readiness: ~3 months.