Machine Learning is a process for learning from existing data, and applying that learning to the current stream of telemetry data and predicting future outcomes. Azure Machine Learning is a cloud-based predictive analysis service, which can be plugged into the current solution, by creating a Machine Learning algorithm, training the model, and then applying it to the new upcoming data stream to have predictability in place. For the current scope of the project, we will skip the integration with Machine Learning, and will focus on integration with the holographic application.
To learn more about these technologies, refer to their documentation:
Azure IoT Hub: https://azure.microsoft.com/en-us/services/iot-hub/
Azure Stream Analytics: https://azure.microsoft.com/en-us/services/stream-analytics/
Azure Storage: https://docs.microsoft.com/en-us/azure/storage/
Azure SQL Database: https://azure.microsoft.com/en-us/services/sql-database
Azure Data Lake: https://azure.microsoft.com/en-us/solutions/data-lake/
Azure Machine Learning: https://azure.microsoft.com/en-us/services/machine-learning/
Azure CosmosDB: https://docs.microsoft.com/en-us/azure/cosmos-db/
Azure IoT Hub: https://azure.microsoft.com/en-us/services/iot-hub/
Azure Stream Analytics: https://azure.microsoft.com/en-us/services/stream-analytics/
Azure Storage: https://docs.microsoft.com/en-us/azure/storage/
Azure SQL Database: https://azure.microsoft.com/en-us/services/sql-database
Azure Data Lake: https://azure.microsoft.com/en-us/solutions/data-lake/
Azure Machine Learning: https://azure.microsoft.com/en-us/services/machine-learning/
Azure CosmosDB: https://docs.microsoft.com/en-us/azure/cosmos-db/