MLOps

MLOps is engineering first — you must write clean, structured, testable code, not scattered scripts.

Almost every production server runs Linux — terminal fluency is a baseline for any deployment.

Git is the backbone of collaboration and automation — every CI/CD pipeline starts from a commit.

Containers ensure your model runs the same everywhere — the end of "works on my machine".

A model must reach users — the most common way is wrapping it behind a fast, reliable API.

Without tracking, you forget which data and params produced your best model — and lose reproducibility.

Automation removes human error — every code or data change is built, tested, and shipped automatically.

As load grows, Kubernetes handles scaling and recovery automatically — the industry standard for production at scale.

Pipelines turn scattered manual steps into automated, schedulable, repeatable workflows.

Models silently degrade as real-world data shifts — monitoring catches it before it does damage.

Code-managed infrastructure is repeatable and reviewable — no more "magic button" nobody remembers clicking.

Production models handle sensitive data — governance and security are prerequisites for trust and legal safety.