Merge pull request #362 from arnaudvl/readme

add README files to outlier detectors

components/outlier-detection/isolation-forest/README.md

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+# Isolation Forest Outlier Detector
+
+## Description
+
+[Anomaly or outlier detection](https://en.wikipedia.org/wiki/Anomaly_detection) has many applications, ranging from preventing credit card fraud to detecting computer network intrusions. The implemented [Isolation Forest](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.IsolationForest.html) outlier detector aims to predict anomalies in tabular data. The anomaly detector predicts whether the input features represent normal behaviour or not, dependent on a threshold level set by the user.
+
+## Implementation
+
+The Isolation Forest is trained by running the ```train.py``` script. The ```OutlierIsolationForest``` class loads a pre-trained model and makes predictions on new data.
+
+A detailed explanation of the implementation and usage of Isolation Forests as outlier detectors can be found in the [isolation_forest_doc](./isolation_forest_doc.ipynb) notebook.
+
+## Running on Seldon
+
+An end-to-end example running an Isolation Forest outlier detector on GCP or Minikube using Seldon to identify computer network intrusions is available [here](./isolation_forest.ipynb).

components/outlier-detection/vae/README.md

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+# Variational Auto-Encoder (VAE) Outlier Detector
+
+## Description
+
+[Anomaly or outlier detection](https://en.wikipedia.org/wiki/Anomaly_detection) has many applications, ranging from preventing credit card fraud to detecting computer network intrusions. The implemented VAE outlier detector aims to predict anomalies in tabular data. The VAE model can be trained in an unsupervised or semi-supervised way, which is helpful since labeled training data is often scarce. The outlier detector predicts whether the input features represent normal behaviour or not, dependent on a threshold level set by the user.
+
+## Implementation
+
+The architecture of the VAE is defined in ```model.py``` and the model is trained by running the ```train.py``` script. The ```OutlierVAE``` class loads a pre-trained model and makes predictions on new data.
+
+A detailed explanation of the implementation and usage of the Variational Auto-Encoder as an outlier detector can be found in the [outlier_vae_doc](./outlier_vae_doc.ipynb) notebook.
+
+## Running on Seldon
+
+An end-to-end example running a VAE outlier detector on GCP or Minikube using Seldon to identify computer network intrusions is available [here](./outlier_vae.ipynb).