Discovering outliers is a critical part of systems monitoring, financial auditing, and more. In this full-day workshop, we will gain an understanding of what constitutes an anomaly or an outlier, starting with a general concept of the terms and then moving into technical definitions. From there, we will build a general-purpose outlier detection API in Python, along with a test suite and demo client. We will extend this API to include a variety of use cases, including multivariate and time series outlier detection, learning about and applying state-of-the-art techniques along the way.

You will learn:

• To understand what outliers and anomalies are
• Discover the state of the art when it comes to outlier detection algorithms
• How to build a web-based service to detect outliers in datasets

Attendee Requirements:

• You must provide your own laptop 
• Your machine must have a modern web browser
• Python is required, preferably the Anaconda distribution but a base installation of Python will work fine.  Python 3.8 or later is recommended
• A Git client is required.  We will interact with a variety of branches in a GitHub repository
• Visual Studio Code is recommended, though other IDEs like PyCharm may also work
• Postman is required for testing
• Docker is not mandatory but may be helpful