IoT platforms are getting increasingly popular as they offer a variety of useful tools and cloud storage options for the data streamed from IoT devices. What does each platform offer?
Microsoft Azure IoT Platform
Azure IoT has a Github repository which was created to help you integrate AI and IoT easily. This code repository provides different code examples on how to use Machine Learning and Azure IoT Edge together. Deep Learning models can be packaged in Azure IoT Edge-compatible Docker containers and then exposed to the data using REST APIs. Alternatively, it can be deployed on the cloud and then used for predictions with REST APIs. Azure IoT has a lot of useful tutorials and case examples which are available on their website.
Google Cloud IoT
Google Cloud IoT is an impressive IoT platform which enables you to connect the data to the machine learning model in many different ways. It offers a complete set of tools for edge/on-premises computing with machine learning capabilities, similarly to Azure IoT. Google has also created a separate AI Platform so you can train the model there, together with a cloud storage option for the IoT data. There are few useful tutorials out there: this one, for example, explains how to deploy the ML model to the IoT device which updates itself on the new upcoming data.
AWS IoT
The well-known AWS also offers AI+IoT solutions. AWS IoT Analytics is especially interesting, as it offers data preparation, visualization, and machine learning tools. Machine learning model can be trained using Amazon Sagemaker and then containerized so it can be used with the data stream from IoT devices. ML models can also be uploaded to devices directly where they run much faster and they can also be automatically updated based on the new incoming data.