The engineering-heavy specialization within data science.
ML engineering leans more on software engineering skill than pure data science does.
Understand supervised and unsupervised learning deeply enough to choose and tune the right approach for a problem.
Often the highest-leverage skill in practical ML work — turning raw data into inputs a model can actually learn from.
Taking a model from notebook to production, then monitoring it for performance drift over time.
Familiarity with how models are served, versioned, and integrated into larger systems.
Complete a project that goes beyond training — actually serving predictions through a working interface.
Every stage above is built into the Machine Learning Track program's curriculum, labs, and projects.
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