Add H2O docs (#206)

docs/SUMMARY.md

@@ -2,6 +2,7 @@
 * [Nussknacker model overview](nussknacker-model.md)
 * [Architecture overview](architecture-overview.md)
 * [MLflow implementation](mlflow.md)
+* [H2O integration](h2o.md)
 * [Testing](testing.md)
 * [Configuration](configuration.md)
 * [Release process](release.md)

docs/h2o.md

@@ -0,0 +1,21 @@
+# H2O integration
+
+[H2O](https://www.h2o.ai/products/h2o/) is one of the leading machine learning platforms.
+It features an open-source server with in-memory implementations of multiple popular algorithms.
+Users can access the server through hosted visual notebooks or using multiple programming languages, including R and Python.
+AutoML functionality helps to automatically select matching model types for provided datasets.
+Created models can be deployed as
+POJOs or MOJOs (_Model Object, Optimized_ - an alternative storage standard by H2O authors)
+for scoring.
+H2O provides both a server endpoint and a set of Java libraries for automated scoring based on deployment files.
+
+## H2O in Prinz
+
+Prinz uses deployment files in MOJO format.
+Client shipped with Prinz allows for loading files from HTTP or local path.
+
+Sample environment presents a minimal deployment:
+- Models `.zip` files are stored on the server (see [`/dev-environment/h2o/exports/`]({{ book.sourcesRootUrl }}dev-environment/h2o/exports/), empty before training);
+- Server exposes list of files as a website. [Minimal Python implementation]({{ book.sourcesRootUrl }}dev-environment/h2o/scripts/serve.py) lists files as a simple website at `localhost:5200`;
+- Application [config]({{ book.sourcesRootUrl }}prinz-sample/src/main/scala/touk/nussknacker/prinz/sample/SampleConfigCreator.scala) includes H2O repository;
+- Nussknacker [configuration file]({{ book.sourcesRootUrl }}dev-environment/nussknacker/opt/prinz-sample/prinz-application.conf) sets endpoint URL;