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Pros of Azure Machine Learning
Pros of NanoNets
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Pros of NanoNets
- Simple API7
- Easy Setup5
- Easy to use4
- Fast Training3
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What is Amazon SageMaker?
A fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale.
What is 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.
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.
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What are some alternatives to Amazon SageMaker, Azure Machine Learning, and NanoNets?
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.
Databricks
Databricks Unified Analytics Platform, from the original creators of Apache Spark™, unifies data science and engineering across the Machine Learning lifecycle from data preparation to experimentation and deployment of ML applications.
Kubeflow
The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.
TensorFlow
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
IBM Watson
It combines artificial intelligence (AI) and sophisticated analytical software for optimal performance as a "question answering" machine.