Add Tags and Descriptions to Models
Annotate models so teammates know what each is for.
Add Tags and Descriptions to Models 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.
Metadata Is Context
A model name and version tell you what exists, not why. Metadata like tags and descriptions adds the context teammates need to trust a model. 🏷️
What a Tag Is
A tag is a key-value pair you attach to a model or version. Think team=fraud or framework=sklearn, simple labels you can filter on later.
Tag the Whole Model
Some facts apply to every version, like the owning team. Set those on the registered model itself so they stay constant across versions.
c.set_registered_model_tag(
"churn-classifier", "team", "growth")Tag a Single Version
Other facts are version-specific, like which dataset trained it. Tag the exact version so each one carries its own provenance.
c.set_model_version_tag(
"churn-classifier", 4,
"dataset", "2024-q3")Descriptions Tell the Story
A description is free text for humans. Use it to explain what the model predicts, who owns it, and any caveats worth knowing.
Describe the Model
Set a model-level description as a short charter: its purpose and intended use, so anyone landing on the page understands it instantly.
c.update_registered_model(
"churn-classifier",
description="Predicts 30-day churn.")Describe a Version
Each version can have its own notes, like what changed since the last one. Record the new feature or fix that motivated this release.
c.update_model_version(
"churn-classifier", 4,
description="Added tenure feature.")Tags Power Search
The real payoff of tags is search. You can list every model owned by a team or built with a framework using a filter string.
c.search_registered_models(
"tags.team = 'growth'")Keep Keys Consistent
Agree on a convention for tag keys across the team. Mixing owner and team or env and stage makes filters miss the models you meant.
Tags Are Not Stages
A tag is just a label; it never changes what serves. Setting env=prod as a tag does not promote a model, only a stage transition does.
Read Metadata Back
Inspecting a version returns its tags and description in code. That lets pipelines make decisions based on the labels you set.
mv = c.get_model_version("churn-classifier", 4)
print(mv.tags, mv.description)Quick Check
Choose the fact best stored on a single version.
Recap
Tags and descriptions turn a bare registry into a searchable, self-documenting catalog. Future you and your teammates will thank present you. 🙌
Frequently asked questions
Is the “Add Tags and Descriptions to Models” lesson free?
Yes — the full text of “Add Tags and Descriptions to Models” 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 “Add Tags and Descriptions to Models”?
Annotate models so teammates know what each is for. 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 “Add Tags and Descriptions to Models” 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
- Register Your First Model Version
- Stages: Staging, Production, Archived
- Add Tags and Descriptions to Models
- Load a Model Back by Name and Stage