What is NanoNets?
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.
NanoNets is a tool in the Machine Learning as a Service category of a tech stack.
Who uses NanoNets?
3 companies reportedly use NanoNets in their tech stacks, including Indra, GridAnts, and ULC Robotics.
11 developers on StackShare have stated that they use NanoNets.
Python, Node.js, PHP, Postman, and C# are some of the popular tools that integrate with NanoNets. Here's a list of all 10 tools that integrate with NanoNets.
Pros of NanoNets
Easy to use
- Image categorization API with less than 30 images per category
- Custom object localization API
- Text deduplication API
- Text categorization API
NanoNets Alternatives & Comparisons
What are some alternatives to NanoNets?
See all alternatives
Azure Machine Learning
Azure Machine Learning is a fully-managed cloud service that enables data scientists and developers to efficiently embed predictive analytics into their applications, helping organizations use massive data sets and bring all the benefits of the cloud to 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.
Amazon Machine Learning
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.
Build And Run Predictive Applications For Streaming Data From Applications, Devices, Machines and Wearables
Amazon Elastic Inference
Amazon Elastic Inference allows you to attach low-cost GPU-powered acceleration to Amazon EC2 and Amazon SageMaker instances to reduce the cost of running deep learning inference by up to 75%. Amazon Elastic Inference supports TensorFlow, Apache MXNet, and ONNX models, with more frameworks coming soon.