各司其职：机器学习团队中的角色

Shipping ML is a relay race, not a solo run. Several roles pass the work along, and MLOps is the smooth handoff between them. 🏃

The data scientist explores data, picks features, and builds the model. Their question is simple: can a model solve this problem well?

The ML engineer turns that experiment into production code: reliable pipelines, APIs, and tests so the model runs at scale.

The data engineer builds the pipes that move and shape data, making sure clean, fresh data shows up where models need it.

The platform engineer owns the shared infrastructure: training clusters, serving, and tooling everyone else builds on top of.

The MLOps engineer connects it all: CI/CD, monitoring, and automation that let models flow from training to production safely.

A product manager defines what success means in business terms, keeping the team aimed at value rather than a prettier accuracy score.

On small teams one person wears many hats. These are roles, not always separate people, so the titles blur a lot in practice.

Most ML pain lives at the handoffs: a notebook tossed over the wall, no environment, no docs. Good MLOps removes that friction.

When everyone uses the same registry and pipelines, handoffs become a link, not a rewrite. Shared tooling is the glue between roles.

You do not need every skill at once. Knowing the roles helps you see where you fit today and where to grow next.

Let us match a key responsibility to the right role.

Data scientists build, ML engineers ship, platform and MLOps folks pave the road. MLOps makes the handoffs between these roles smooth. 🤝