![]() Sensitivity - "Sensitivity" is from 0 to 99, defines how sensitively the back-end API detects anomalies.We have the ability to work with these parameters: "Entire" mode creates and trains one model for the entire time series and detects anomalies over the entire model and all data points at once.Īnomaly detection can be configured by entering parameter values in the request. For example, it is suitable for data from Google Analytics. Each point in time is analyzed based on a model trained by previous data. The output after calling this API is a set of parameters that say whether it is an anomaly at a given point in time and whether it was a decrease or an increase.Īzure anomaly detector REST API works in two optional modes. Examples include Jupyter Notebooks, Postman, Visual Studio, or Azure-enabled services such as Databricks, or Azure Machine Learning with integrated notebooks.Īzure anomaly detection REST API is a service provided by Azure as one of the Cognitive services accessible via the Azure portal where you can obtain the authorization key and endpoint that are necessary for later calls. The Rest API can be called from any tool that can call GET requests. The only condition is that the data must be in a defined structure: timestamp + selected value The Azure anomaly detector REST API is a service that provides the ability to detect anomalies in time series from any data source. The predicted value is then compared to the actual measured value and the points at which it is actually measured are identified. This prediction always has a certain sensitivity/confidence interval in which it moves (light blue area in the image below). ![]() In general, the anomaly detection algorithm predicts the value at a selected point based on previous observations. In the first part Azure Cognitive Services demos in practice, we focused on the Anomaly Detector service and AML Notebooks. It is a package of 25 tools that through APIs allow the developers to add a variety of features to their applications. All it takes is an API call to embed the ability to see, hear, speak, search, understand, and accelerate decision-making. They are available widely to developers without requiring machine-learning expertise. ![]() Azure Cognitive Servicesīefore we explain the practical use of Azure Cognitive Services, introducing all areas can give a good picture of how technology collaboration and accessibility can create an empowering experience for the end-user.Īzure Cognitive Services is a set of machine learning algorithms developed to solve problems in the field of Artificial Intelligence (AI). ![]() We started the practical part of the workshop with an overview of the areas of Azure Cognitive Services and then we moved to use cases of Anomaly Detector, text, and vision services. At the first sight, it may seem like too much science behind, yet the algorithms can be implemented easier than you might think and help you with your marketing or occupational safety within your business. Technology incorporating machine learning, natural language processing, human-computer interaction, and more are described as cognitive computing. If you combine artificial intelligence with signal processing, you can improve everyday tasks such as safety equipment checks, anomaly detection, or reading documents.
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