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+# Usage templates
+
+This page provides code templates to copy-paste, change and use. For the context
+and motivations check out [Get Started](/doc/get-started).
+
+## Save model
+
+<toggle>
+<tab title="From code">
+
+Just replace model saving code with
+
+```py
+from mlem.api import save
+save(
+    clf,
+    "models/rf",
+    sample_data=df,
+)
+```
+
+</tab>
+
+<tab title="From existing file">
+
+Just run
+
+```cli
+$ mlem import models/rf models/rf.mlem --type pickle
+```
+
+</tab>
+
+</toggle>
+
+## Use model
+
+<toggle>
+<tab title="Serve with REST API">
+
+Online inference is usually done by exposing your model. MLEM allows to build a
+FastAPI server, so you can get predictions via REST API.
+
+<toggle>
+<tab title="Serve model">
+
+To start up a FastAPI server exposing your model run:
+
+```cli
+$ mlem serve fastapi --model models/rf
+⏳️ Loading model from models/rf.mlem
+Starting fastapi server...
+🖇️  Adding route for /predict
+🖇️  Adding route for /predict_proba
+🖇️  Adding route for /sklearn_predict
+🖇️  Adding route for /sklearn_predict_proba
+Checkout openapi docs at <http://0.0.0.0:8080/docs>
+INFO:     Started server process [16696]
+INFO:     Waiting for application startup.
+INFO:     Application startup complete.
+INFO:     Uvicorn running on http://0.0.0.0:8080 (Press CTRL+C to quit)
+```
+
+</tab>
+
+<tab title="Build model">
+
+Build a docker image with FastAPI server baked in:
+
+```cli
+$ mlem build docker docker-builder.mlem \
+    --model models/rf \
+    --image.name mlem-model
+⏳️ Loading model from models/rf.mlem
+🛠 Building MLEM wheel file...
+💼 Adding model files...
+🛠 Generating dockerfile...
+💼 Adding sources...
+💼 Generating requirements file...
+🛠 Building docker image mlem-model:latest...
+✅  Built docker image mlem-model:latest
+```
+
+Now you can distribute and run the Docker image. Running the container with
+`$ docker run -p 8080:8080 mlem-model:latest` will start FastAPI server.
+
+</tab>
+
+<tab title="Deploy model">
+
+To deploy a model to Heroku (or Kubernetes, Sagemaker, or run it in docker
+container):
+
+```
+$ mlem deployment run heroku app.mlem \
+  --model models/rf \
+  --app_name example-mlem-get-started-app
+⏳️ Loading model from models/rf.mlem
+⏳️ Loading deployment from app.mlem
+🛠 Creating docker image for heroku
+  🛠 Building MLEM wheel file...
+  💼 Adding model files...
+  🛠 Generating dockerfile...
+  💼 Adding sources...
+  💼 Generating requirements file...
+  🛠 Building docker image registry.heroku.com/example-mlem-get-started-app/web...
+  ✅  Built docker image registry.heroku.com/example-mlem-get-started-app/web
+  🔼 Pushing image registry.heroku.com/example-mlem-get-started-app/web to registry.heroku.com
+  ✅  Pushed image registry.heroku.com/example-mlem-get-started-app/web to registry.heroku.com
+🛠 Releasing app example-mlem-get-started-app formation
+✅  Service example-mlem-get-started-app is up. You can check it out at https://example-mlem-get-started-app.herokuapp.com/
+```
+
+</tab>
+
+</toggle>
+
+## Send requests to model
+
+<toggle>
+<tab title="CLI inference example">
+
+### ⚙️ Expand for a CLI inference example
+
+You can launch requests at the running server from a terminal, Using CLI
+commands like `curl` or `httpie`.
+
+For example:
+
+```cli
+$ curl -X 'POST' \
+  'http://0.0.0.0:8080/predict_proba' \
+  -H 'accept: application/json' \
+  -H 'Content-Type: application/json' \
+  -d '{
+  "data": {
+    "values": [
+      {
+        "sepal length (cm)": 0,
+        "sepal width (cm)": 1,
+        "petal length (cm)": 2,
+        "petal width (cm)": 3
+      }
+    ]
+  }
+}'
+[[0.47,0.24,0.29]]
+```
+
+The Swagger UI even helps to generate `curl` commands similar to the above
+interactively.
+
+</tab>
+
+<tab title="API inference example">
+
+TODO
+
+</tab>
+</toggle>
+
+</tab>
+<tab title="Use for batch scoring">
+
+<toggle>
+<tab title="Apply from CLI">
+
+TODO: Showcase `mlem apply`
+
+</tab>
+<tab title="Build a docker image and apply by running it">
+
+TODO: Showcase `mlem build` and `mlem apply` inside
+
+</tab>
+</toggle>
+
+</tab>
+<tab title="Use in Python process">
+
+<toggle>
+<tab title="Load model in Python process">
+
+TODO: Showcase `mlem.api.load()`
+
+</tab>
+<tab title="Build a Python package with a model">
+
+TODO: Showcase `mlem build python` and `from package import` inside
+
+</tab>
+</toggle>
+
+</tab>
+</toggle>