Q. How does the IP Camera AI SaaS Solution work?

The solution automatically provisions cloud resources and deploys 3 built-in models to SageMaker endpoints. Users can make the use of the AI features by sending requests to the APIs.
In addition, users can also build customized models based on their own dataset, and package it with ECS container image, finally integrate it in the solution.

Q. What is user experience of using the IP Camera AI SaaS Solution?

You can deploy the solution directly from the solution page or from the GitHub repository. It takes around 20 mins to deploy the AI SaaS solution into your Amazon Web Services account. In term of using the SaaS, you can send image data in the HTTP POST input request and receive the response with information such as bounding boxes and confidence of the face/body in the image, etc.

Q. How much does the IP Camera AI SaaS Solution cost?

The cost is related to the usage of cloud resources and it can be divided into two parts: compute and storage. The cost is positively related to the number of machines you configured using the deployment for AI inference and the frequency of requests.

Q. How much development effort needed to use the IP Camera AI SaaS Solution?

The solution provides an out-of-box experience, thus, no extra effort needed. If the out-of-box features do not meet your requirement, you can provide your own customized models, package them in ECR images and use them in the solution, by referencing the source code in GitHub.

Q. How is the latency of the playback system for IP Camera SDK?

Assume the network speed is normal, the end-to-end real-time playback latency is around 14-20 seconds.

Q. What is the limitation of using IP Camera SDK?

The built-in clock in camera device should be accurate. The maximum offset cannot be over 5 minutes. Otherwise, the SDK will return 403 error when uploading video clips. In the case of losing video clips, please check the network latency.

Q. Does the IP Camera SDK support multi-tenant?

There are multiple approaches to implement a multi-tenant system. For example, (1) In the SDK, you can configure prefixes for S3 bucket that stores video clips, using parameters such as device names, SoC types, etc. This gives you the flexibility to detemine your own multi-tenant strategy. (2) You can also use the Amazon IoT Credential Provider to get device certificate for each device. 

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