Who Does What: Roles in an ML Team
Data scientist, ML engineer, and platform roles explained.
Who Does What: Roles in an ML Team is a free MLOps Academy lesson on CoddyKit — lesson 3 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the MLOps Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
A Relay Race
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
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
The ML engineer turns that experiment into production code: reliable pipelines, APIs, and tests so the model runs at scale.
The Data Engineer
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
The platform engineer owns the shared infrastructure: training clusters, serving, and tooling everyone else builds on top of.
The MLOps Engineer
The MLOps engineer connects it all: CI/CD, monitoring, and automation that let models flow from training to production safely.
The Product Side
A product manager defines what success means in business terms, keeping the team aimed at value rather than a prettier accuracy score.
Roles Overlap
On small teams one person wears many hats. These are roles, not always separate people, so the titles blur a lot in practice.
Handoffs Cause Pain
Most ML pain lives at the handoffs: a notebook tossed over the wall, no environment, no docs. Good MLOps removes that friction.
Shared Tools Help
When everyone uses the same registry and pipelines, handoffs become a link, not a rewrite. Shared tooling is the glue between roles.
Where You Fit
You do not need every skill at once. Knowing the roles helps you see where you fit today and where to grow next.
Quick Check
Let us match a key responsibility to the right role.
Recap
Data scientists build, ML engineers ship, platform and MLOps folks pave the road. MLOps makes the handoffs between these roles smooth. 🤝
Frequently asked questions
Is the “Who Does What: Roles in an ML Team” lesson free?
Yes — the full text of “Who Does What: Roles in an ML Team” is free to read here on the web, and the MLOps Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the MLOps Academy course, upgrade to CoddyKit PRO.
What will I learn in “Who Does What: Roles in an ML Team”?
Data scientist, ML engineer, and platform roles explained. You practise MLOps Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start MLOps Academy?
No prior experience is required. MLOps Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 3 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Who Does What: Roles in an ML Team” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this MLOps Academy lesson?
Yes. Every MLOps Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- MLOps vs Plain Machine Learning
- The ML Lifecycle in One Picture
- Who Does What: Roles in an ML Team
- Your MLOps Maturity Checklist