Amazon Rekognition vs. Azure OpenAI Service

Amazon Rekognition is well suited for all image and videos analysis. Also, deep learning projects for image and video can easily be done using this. It is very easy to use so a beginner can also use it.

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If you're looking for a managed OpenAI API service, then Azure OpenAI Service is a good choice.

It's fully compatible with OpenAI API, has lots of models to choose from, lots of parameters to configure to suite your needs.

The documents are well maintained, with examples to get started.

You can also setup firewall to restrict access to the API to certain IP addresses, like those of your VPCs.

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• Image tagging is very good.
• Object detection is precise.
• Video tagging has become very easy using Rekognition.

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• Generating SQL
• Generating Images
• Sensible Response

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• The cost is bit more for small scale companies.
• For text processing, OCR is not available in amazon rekognition.
• Only facial search is available in image search.

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• The user interface is similar to the Azure, so it's bit laggy, they should improve it.
• The chat models sometimes return nothing, I guess they are still testing them.
• The models we use have quota limitation on tokens and number of requests.

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Very much suitable for many applications where the image processing features are secondary and independent of any domain. This makes it a general solution and the recognition features are returned in a JSON object in response to the API called made which is a very simple process.

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I think it's a good product and appreciate the addition secure guard rails that running it in Azure provide. However, I still struggle at times to get to the right resources for support and region-based capacity can also be a challenge.

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Support of Amazon is great. So far we are having a great experience using it.

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It's easy to implement and its documentation is great compared to CNTK.

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1. Open AI is best at giving accurate answers. 2. It is secure and more trustworthy 3. Most of our client using Azure cloud so it becomes go to choice for them. 4. Scalable as it handles 1000s of request per minute. 5. SDKs are easy to use and well documented.

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• The best thing it made our work fast.
• It reduces the chance of failure of a project which is based on image or video processing.
• We can assign anyone in our team to work on this as it very easy to use.

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• Our test group of 100 people created more than 200 conversations within the first day
• People were excited & asked to be included to the test group once words spread that we have an internal ChatGPT portal
• People started to create & share their own assistants (prompt engineering) for various purposes like code review, marketing, email, etc.

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