Collaborating Authors

Anomaly detection using machine learning in Azure Stream Analytics


Azure Stream Analytics is a fully managed serverless offering on Azure. With the new Anomaly Detection functions in Stream Analytics, the whole complexity associated with building and training custom machine learning (ML) models is reduced to a simple function call resulting in lower costs, faster time to value, and lower latencies.

Anomaly detection using built-in machine learning models in Azure Stream Analytics


Built-in machine learning (ML) models for anomaly detection in Azure Stream Analytics significantly reduces the complexity and costs associated with building and training machine learning models. This feature is now available for public preview worldwide both in the cloud and on IoT Edge. Azure Stream Analytics is a fully managed serverless PaaS offering on Azure that enables customers to analyze and process fast moving streams of data, and deliver real-time insights for mission critical scenarios. Developers can use a simple SQL language (extensible to include custom code) to author and deploy powerful analytics processing logic that can scale-up and scale-out to deliver insights with milli-second latencies. Many customers use Azure Stream Analytics to continuously monitor massive amounts of fast-moving streams of data in order to detect issues that do not conform to expected patterns and prevent catastrophic losses.

A Scalable End-to-End Anomaly Detection System using Azure Batch AI


To complete the pipeline of an end-to-end solution I've created a walkthrough on GitHub that includes submitting and scheduling prediction jobs in addition to training of models. The solution comprises several Azure cloud services and Python code that interacts with those services. The scheduling component allows continuous training and scoring in a production environment. The diagram below shows the proposed solution architecture where the main components are Azure services that can easily connect to each other through configuration or SDKs. This is a general solution and only one way of designing predictive maintenance solutions.

Azure Cosmos DB for AI Engineers


In this "Azure Cosmos DB for AI Engineers" blog post, you will learn how AI Engineers can use Azure Cosmos DB to support their AI solutions, focusing on storing and analyzing unstructured or semi-structured data. AI Engineers design and implement intelligent apps and agents that simulate human perception using cognitive services, machine learning, and knowledge mining. Typical scenarios are anomaly detection, language understanding, text mining, search, among others. Let's see why Azure Cosmos DB is the perfect database for AI Architectures on Azure. Cognitive Services bring AI within reach of every developer--without requiring machine-learning expertise.

Deep Anomaly Detection Using Geometric Transformations

Neural Information Processing Systems

We consider the problem of anomaly detection in images, and present a new detection technique. Given a sample of images, all known to belong to a normal'' class (e.g., dogs), we show how to train a deep neural model that can detect out-of-distribution images (i.e., non-dog objects). The main idea behind our scheme is to train a multi-class model to discriminate between dozens of geometric transformations applied on all the given images. The auxiliary expertise learned by the model generates feature detectors that effectively identify, at test time, anomalous images based on the softmax activation statistics of the model when applied on transformed images. We present extensive experiments using the proposed detector, which indicate that our algorithm improves state-of-the-art methods by a wide margin.