Amazon Machine Learning vs NanoNets: What are the differences?

Developers describe Amazon Machine Learning as "Visualization tools and wizards that guide you through the process of creating ML models w/o having to learn complex ML algorithms & technology". This new AWS service helps you to use all of that data you’ve been collecting to improve the quality of your decisions. You can build and fine-tune predictive models using large amounts of data, and then use Amazon Machine Learning to make predictions (in batch mode or in real-time) at scale. You can benefit from machine learning even if you don’t have an advanced degree in statistics or the desire to setup, run, and maintain your own processing and storage infrastructure. On the other hand, NanoNets is detailed as "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 Machine Learning and NanoNets belong to "Machine Learning as a Service" category of the tech stack.

Some of the features offered by Amazon Machine Learning are:

• Easily Create Machine Learning Models
• From Models to Predictions in Seconds
• Scalable, High Performance Prediction Generation Service

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