Amazon SageMaker vs NanoNets: What are the differences?
Amazon SageMaker: Accelerated Machine Learning. A fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale; NanoNets: Machine learning API with less data. Build a custom machine learning model without expertise or large amount of data. Just go to nanonets, upload images, wait for few minutes and integrate nanonets API to your application.
Amazon SageMaker and NanoNets belong to "Machine Learning as a Service" category of the tech stack.
Some of the features offered by Amazon SageMaker are:
- Build: managed notebooks for authoring models, built-in high-performance algorithms, broad framework support
- Train: one-click training, authentic model tuning
- Deploy: one-click deployment, automatic A/B testing, fully-managed hosting with auto-scaling
On the other hand, NanoNets provides the following key features:
- Image categorization API with less than 30 images per category
- Custom object localization API
- Text deduplication API
What is Amazon SageMaker?
What is NanoNets?
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Why do developers choose Amazon SageMaker?
What are the cons of using Amazon SageMaker?
What are the cons of using NanoNets?
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