Scarlet is working on the Azure IoT project to implement the machine learning model for anomaly detection.
The Anomaly detection ML model is required to be developed for both anomalies - temporary and persistent.
Which of the following Azure resources can she select for event processing and integration for the Anomaly detection model?
Click on the arrows to vote for the correct answer
A. B. C. D.Correct Answer: D.
Scarlet can select Azure Stream Analytics for event processing and integration for the Anomaly detection model.
Azure Stream Analytics is a fully-managed cloud service for real-time event processing. It allows users to easily develop and run real-time data processing solutions on data streams from sources such as IoT devices, social media, and other applications.
Azure Stream Analytics is a suitable choice for event processing and integration in the Anomaly detection model because it can handle both temporary and persistent anomalies. With Azure Stream Analytics, Scarlet can set up anomaly detection rules to identify both types of anomalies and then use these rules to trigger alerts or other actions.
Azure ML Workbench, Azure Databricks Spark MLlib, and Azure ML Studio are all powerful tools for machine learning and data analysis. However, they are not specifically designed for event processing and integration in real-time scenarios like Azure Stream Analytics.
Azure ML Workbench is an integrated development environment (IDE) for building and deploying machine learning models. It is used for developing, training, and deploying machine learning models on Azure. It provides data preparation, model selection, and experiment management capabilities.
Azure Databricks Spark MLlib is a machine learning library that provides scalable algorithms for data processing, feature engineering, and model training. It is based on Apache Spark, which is a distributed computing framework for big data processing.
Azure ML Studio is a web-based visual interface that provides a drag-and-drop interface for building machine learning models. It includes pre-built algorithms for classification, regression, clustering, and anomaly detection. However, it is primarily designed for batch processing rather than real-time event processing.