AWS IoT Core vs. C3 AI Platform

End-to-end encryption is an amazing feature because we use IoT to connect to various devices in order to gather data/ stats in real-time. We're able to publish solutions with ease and at a faster rate because of AWS IoT Core. However, its inability to interact with other IoT tools is a big con that I would like them to improve upon.

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For consultants like me, who are not interested in generic LLM's with very deployment costs and payback times, industry specific applications are essential. We are time-bound to deliver value to our clients whether it is improved productivity or revenue uplift, and for this particular reason C3 AI Platform is a particularly good choice.

• AWS IoT Core integrates power analytics and an AI solution for sensor data processing.
• Great protocol support including HTTPs and MQTT required to connect resource constraint IoT devices to cloud
• High security standards during data transmission
• Scalability and a great user community

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• Ability to integrate with Ethercalc software solution
• Ability to introduce NFC toolchains and systems which could be utilized in iot modular gateway solutions like drones
• Ability to reduce latency as the solution scales is very much needed
• Hybrid solution which can integrate other existing iot solutions into AWS iot core

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I give AWS IoT Core's overall usability this rating because it is very easy to use and is enjoyed by all of our staff. The only problem is that it sometimes glitches and it freezes a lot. So overall, the usability of AWS IoT Core is very good, and we will continue to use it.

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I’d give it 7.5/10. Its model-driven architecture is powerful for scaling enterprise AI, at pace but it definitely needs some heavy-lifting. The platform can be hard to grasp initially and the steep learning curve, makes change management very important. The framework can be a bit rigid for industry agnostic developers used to flexible, open-source tools. It is excellent for data orchestration but is not as lean as some of the low-code competitors.

It covers all the aspects of IoT services required for an IoT company. It supports all the industry-wide protocols for secure data transmission and integrates powerful AL and ML technology for data analytics. For data storage, Amazon S3 is a great solution. Strong tech support and user community. Since it is widely used as compared to other products, there is an abundance of training and learning material on the web.

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Azure IoT service provides more or less the same services as compared to AWS IoT core, however the costing of AWS lead us to continued usage of IoT core over Azure IoT services. Also, considering our existing technology stack is on AWS, it was a natural selection for better integration and ease of use.

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C3 AI Platform offers much faster deployment through pre-built, industry-specific apps, and comfortably beats the others when it comes to time to deployment and scalability. Palantir on the other hand requires heavy custom engineering. C3 AI Platform does lack the open-source flexibility of Databricks and the cloud native scale of Vertex. I would prefer C3 AI Platform for turnkey enterprise solutions, but for other use cases it can be a bit more complex vs the other three due to its "back-box" environment.

• Gives confidence to prototype iot solutions across lots of devices in little time
• Ability to meet requirements for managing a variety of communication protocols
• Learning iot analytics and visualization for faster insights enablement
• Onboarding devices and managing quickly without building inhouse solutions

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