Anomaly Detection is a machine learning technique used for identifying rare items, events, or observations by checking for rows in the table that differ significantly from the majority of the rows. Typically, the anomalous items will translate to some kind of problem such as bank fraud, a structural defect, medical problem or error. Some common business use cases for anomaly detection are: Fraud detection (credit cards, insurance, etc.) using financial data.

Hopsworks 1.x series brings many new features and improvements, ranging from services such as the Feature Store and Experiments, to enhanced support for distributed stream processing and analytics with Apache Flink and Apache Beam, to building Deep Learning pipelines with TensorFlow Extended (TFX), to code versioning support for Jupyter notebooks with Git, to all-new provenance/lineage of data across all steps of a data engineering and data science. We are also excited that Hopsworks 1.x is the back-bone of the all new Managed Hopsworks platform for AWS, Hopsworks.ai Hopsworks 1.x brings significant Feature Store improvements ranging from updated UI components to connectivity with external systems and feature discovery. Users of Hopsworks Enterprise can now easily connect to the Feature Store from their Databricks notebooks and Amazon Sagemaker. Documentation for connecting with these two platforms can be found at hopsworks.readthedocs.io

The way I did it was that I tried to read all the [online documentation][doc link] and work with the examples as described there. While doing this I encountered many questions and asked around about them. In this process I felt the need of a tutorial for someone with background like mine. In this tutorial, I try to cover the things I found most relevant from my own experience, some of which are explained by the Azure Machine Learning Studio documentation and others are not. The purpose is to help you grasp the core elements of using Azure Machine Learning Studio in about 3-4 hours: managing workspace, fitting models, evaluating models, setting up web service, consuming web service, and running R scripts.