This repository contains the implementation of the Anomaly Detection Accelerator which is the technique of identifying rare events or observations which can raise suspicions by being statistically different from the rest of the observations. Such “anomalous” behavior typically translates to some kind of a problem like a:
- credit card fraud,
- failing machine in a server,
- a cyber-attack,
- variation in financial transactions,
- and so on.
Common Anomaly Detection techniques are difficult to implement on very large sets of Data. The Anomaly Detection Accelerator, leverages the iJungle technique from Dr Ricardo Castro, which solves this challenge, enabling anomaly detection on large sets of data.
- This repository includes the implementation od the iJungle anomaly detection technique to be executed in an on-premise setting or in the cloud
- Also it includes a tutorial notebook that guides its use leveraging Azure Machine Learning capabilities like parallel training, and parallel evaluation to be able to reach high volume data analysis.
- It include examples of how to use it as notebooks in Azure Databricks
In order to successfully complete your solution, you will need to have access to and or provisioned the following:
- Access to an Azure subscription
- Access to an Azure Machine Learning Workspace with contributor rights
iJungle can run on a single machine and in a distributed way for data intensive scenarios using Azure Machine Learning (AML) under Linux environment like Ubuntu. We reccomend that it is used under an AML Workspace.
Once cloned the git repository, under Anomaly Detection folder execute:
make all
This is going to create iJungle whl file under dist folder and install it using pip.
Once installed, open iJungle-tutorial.ipynb and follow the notebook.
| File/Folder | Description |
|---|---|
notebooks |
iJungle quick-start notebook(iJungle-tutorial.ipynb), including single & parallel processing |
src/iJungle |
iJungle source codes |
operation |
iJungle source codes used for parallel processing |
data |
Sample datasets used in notebooks |
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