diff --git a/content/docs/dvclive/index.md b/content/docs/dvclive/index.md
index da6b6a632b8..fa22dcb8ffe 100644
--- a/content/docs/dvclive/index.md
+++ b/content/docs/dvclive/index.md
@@ -5,7 +5,8 @@ metrics in machine learning projects. Use it to log metrics during training.
 
 dvclive is integrated seamlesly with dvc and logs produced by it can be fed to
 `dvc plots` command. Even though, one does not need dvc to visualize dvclive
-logs, as they are saved into easily readable tsv format.
+logs, as they are saved into easily parsable tsv format, feel free to apply
+custom visualization methods.
 
 We have created dvclive with two principles in mind:
 
diff --git a/content/docs/dvclive/usage.md b/content/docs/dvclive/usage.md
index e69de29bb2d..d1e92432cc6 100644
--- a/content/docs/dvclive/usage.md
+++ b/content/docs/dvclive/usage.md
@@ -0,0 +1,146 @@
+# Usage
+
+We will use sample [MNIST classification](http://yann.lecun.com/exdb/mnist/)
+training code in order to see how one can introduce `dvclive` into the workflow.
+In order to run the example,
+[keras](https://keras.io/about/#installation-amp-compatibility) is required.
+
+The code:
+
+```python
+from keras.datasets import mnist
+from keras.models import Sequential
+from keras.layers.core import Dense, Activation
+from keras.utils import np_utils
+
+def load_data():
+    (x_train, y_train), (x_test, y_test) = mnist.load_data()
+
+    x_train = x_train.reshape(60000, 784)
+    x_test = x_test.reshape(10000, 784)
+    x_train = x_train.astype('float32')
+    x_test = x_test.astype('float32')
+    x_train /= 255
+    x_test /= 255
+    classes = 10
+    y_train = np_utils.to_categorical(y_train, classes)
+    y_test = np_utils.to_categorical(y_test, classes)
+    return (x_train, y_train), (x_test, y_test)
+
+def get_model():
+    model = Sequential()
+    model.add(Dense(512, input_dim=784))
+    model.add(Activation('relu'))
+
+    model.add(Dense(10, input_dim=512))
+
+    model.add(Activation('softmax'))
+
+    model.compile(loss='categorical_crossentropy',
+    metrics=['accuracy'], optimizer='sgd')
+    return model
+
+
+(x_train, y_train), (x_test, y_test) = load_data()
+model = get_model()
+
+model.fit(x_train,
+          y_train,
+          validation_data=(x_test, y_test),
+          batch_size=128,
+          epochs=3)
+```
+
+Run the code to verify the training is running.
+
+In this example we are training the `model` for 10 epochs. Lets use `dvclive` to
+log the `accuracy`, `loss`, `validation_accuracy` and `validation_loss` after
+each epoch, so that we can observe how our training progresses.
+
+In order to do that, we will need to provide proper
+[`Callback`](https://keras.io/api/callbacks/) for `fit` method:
+
+```python
+from keras.callbacks import Callback
+import dvclive
+class DvcLiveCallback(Callback):
+    def on_epoch_end(self, epoch: int, logs: dict = None):
+        logs = logs or {}
+        for metric, value in logs.items():
+            dvclive.log(metric, value)
+        dvclive.next_step()
+```
+
+We created callback, that, on the end of each epoch, will iterate over gathered
+metrics (`logs`) and use `dvclive.log` function to log their respective value.
+After logging the metrics, we call `dvclive.next_step` function to signal
+`dvclive` that we are done with metrics logging for current epoch.
+
+In order to make it work with the training code, we need to do one more change,
+we need to replace:
+
+```python
+model.fit(x_train,
+          y_train,
+          validation_data=(x_test, y_test),
+          batch_size=128,
+          epochs=10)
+```
+
+with:
+
+```python
+dvclive.init("training_metrics")
+model.fit(x_train,
+          y_train,
+          validation_data=(x_test, y_test),
+          batch_size=128,
+          epochs=3,
+          callbacks=[MetricsCallback()])
+```
+
+We call `dvclive.init` to tell `dvclive` to write metrics under
+`training_metrics` directory. We also provide `callbacks` argument for `fit`
+method with newly created callback.
+
+Rerun the code.
+
+After running the code, you can see that `training_metrics` directory has been
+created.
+
+```bash
+$ ls
+training_metrics  training_metrics.json  train.py
+```
+
+Besides directory, `training_metrics.json` has been created. It's file
+containing information about latest training step. You can prevent its creation
+during `dvclive.init` call:
+
+```python
+dvclive.init('training_metrics', summary=False)
+```
+
+`training_metrics` directory contains `*.tsv` files with names respective to
+metrics logged during training:
+
+```bash
+$ tree training_metrics
+
+training_metrics
+├── accuracy.tsv
+├── loss.tsv
+├── val_accuracy.tsv
+└── val_loss.tsv
+```
+
+Each of the files contains metric values logged in every training step:
+
+```bash
+$ training_metrics/accuracy.tsv
+
+timestamp	step	accuracy
+1614129197192	0	0.7612833380699158
+1614129198031	1	0.8736833333969116
+1614129198848	2	0.8907166719436646
+```