Deep learning for predictive maintenance with Long Short Term Memory Networks
Deep learning has proven to show superior performance in certain areas such as object recognition and image classification. It has also gained popularity in other domains such as finance where time-series data plays an important role. Similarly, in predictive maintenance, the data is collected over time to monitor the state of an asset with the goal of finding patterns to predict failures which can benefit from certain deep learning algorithms. Among the deep learning networks, Long Short Term Memory (LSTM) networks are especially appealing to the predictive maintenance domain since they are very good at learning from sequences. This fact lends itself to their applications using time series data by making it possible to look back for longer periods of time to detect failure patterns. Read about it on the Azure blog.DIY Drone Programming - By Auzre IoT Squad
Hi All, This is a session series which we are presenting. Drone Programming. In the First Session we demonstrated how to buid a drone using low cost devices. Next time we are expecting link this Drone with Azure IoT to process advance requirement. Please check and follow this. Hope it will be usefull to may people. Session URL : http://www.azureiotsquad.com/2017/05/31/azure-iot-squad-session-06-diy-drone-programming/ Best Regards, ShameeraHubs, Sharding and Partitioning
It seems there is a some misunderstanding related to sharding and partitioning when talking about IoT and Microsoft Azure platform. Here is my recommendation about how we should thnk about this. EventHub uses internally messaging feature called 'partitioning'. To understand how that works I would recommend to read more about this in context of service bus. It basically enables multiple message brokers instead of a single one. So, this is called partitioing even if some people might call this shrading. IotHub also uses same (or similar) technic as EventHub. However, when we talk about IotHub 'shards' we do not think about partitioning on message level. It is rather using of multiple IotHubs inside of same solution. This is not some specific technology under the hub. To use multiple IotHubs inside of a single solution, You should decide how device provisioning is done. Dev1-Hub1, Dev2->Hub2 etc.
