Amazon TensorFlow enables developers to quickly and easily get started with deep learning in the cloud.
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Azure OpenAI Service
Score 8.3 out of 10
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Azure OpenAI Service, a service from Microsoft's Azure suite available in preview, includes pre-generated AI models that enable users to apply advanced coding and language models to a variety of use cases, enabling new reasoning and comprehension capabilities for building applications. Users can apply these coding and language models to a variety of use cases, such as writing assistance, code generation, and reasoning over data.
A well-suited scenario for using AWS Tensor Flow is when having a project with a geographically dispersed team, a client overseas and large data to use for training. AWS Tensor Flow is less appropriate when working for clients in regions where it hasn't been allowed yet for use. Since smaller clients are in regions where AWS Tensor Flow hasn't been allowed for use, and those clients traditionally don't have enough hardware, this situation deters a wider use of the tool.
Amazon Elastic Compute Cloud (EC2) allows resizable compute capacity in the cloud, providing the necessary elasticity to provide services for both, small and medium-sized businesses.
Tensor Flow allows us to train our models much faster than in our on-premise equipment.
Most of the pre-trained models are easy to adapt to our clients' needs.
SageMaker isn't available in all regions. This is complicated for some clients overseas.
For larger instances, when using a GPU, it takes a while to talk to a customer service representative to ask for a limit increase. Given this, it's recommendable to ask in advance for a limit increase in more expensive and larger cases; otherwise, SageMaker will set the limit to zero by default.
Since the data has to be stored in S3 and copied to training, it doesn't allow to test and debug locally. Therefore, we have to wait a lot to check everything after every trail.
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.
Microsoft Azure is better than Amazon Tensor Flow because it provides easier and pre-built capabilities such as Anomaly Detection, Recommendation, and Ranking. AWS is better than IBM Watson ML Studio because it has direct and prebuilt clustering capabilities AWS, like IBM Watson ML Studio, has powerful built-in algorithms, providing a stronger platform when comparing it with MS Azure ML Services and Google ML Engine.
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.
