Three features now available in GA enable you to seamlessly log inference data to a central location, set up event-driven applications, processes, or workflows based on Azure Machine Learning events, and utilize lifecycle management of features from creation through archival.
Log inference data to a central location with the Model Data Collector (MDC) : You can now seamlessly log inference input and output data to the Azure Blob Storage location of your choice. This data can be used for compliance, auditing, or monitoring.
React to event-driven applications, processes, and workflows with EventGrid integration : You can now use Event Grid with modern serverless architectures to react to Azure Machine Learning events, such as the completion of training runs, the registration and deployment of models, and the detection of data drift.
Network isolation in managed feature store : You can now use the managed feature store (with network isolation) to experiment and ship models faster, increase reliability of your models, and reduce your operational costs.
