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Amazon Machine Learning
Amazon Machine Learning

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NanoNets
NanoNets

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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鈥檝e 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鈥檛 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
- No public GitHub repository available -
- No public GitHub repository available -

What is Amazon Machine Learning?

This new AWS service helps you to use all of that data you鈥檝e 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鈥檛 have an advanced degree in statistics or the desire to setup, run, and maintain your own processing and storage infrastructure.

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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Why do developers choose Amazon Machine Learning?
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        What companies use Amazon Machine Learning?
        What companies use NanoNets?

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        What tools integrate with Amazon Machine Learning?
        What tools integrate with NanoNets?
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          What are some alternatives to Amazon Machine Learning and NanoNets?
          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.
          Apache Spark
          Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
          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.
          RapidMiner
          It is a software platform for data science teams that unites data prep, machine learning, and predictive model deployment.
          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.
          See all alternatives
          Decisions about Amazon Machine Learning and NanoNets
          Julien DeFrance
          Julien DeFrance
          Principal Software Engineer at Tophatter | 2 upvotes 14.4K views
          atSmartZipSmartZip
          Amazon SageMaker
          Amazon SageMaker
          Amazon Machine Learning
          Amazon Machine Learning
          AWS Lambda
          AWS Lambda
          Serverless
          Serverless
          #FaaS
          #GCP
          #PaaS

          Which #IaaS / #PaaS to chose? Not all #Cloud providers are created equal. As you start to use one or the other, you'll build around very specific services that don't have their equivalent elsewhere.

          Back in 2014/2015, this decision I made for SmartZip was a no-brainer and #AWS won. AWS has been a leader, and over the years demonstrated their capacity to innovate, and reducing toil. Like no other.

          Year after year, this kept on being confirmed, as they rolled out new (managed) services, got into Serverless with AWS Lambda / FaaS And allowed domains such as #AI / #MachineLearning to be put into the hands of every developers thanks to Amazon Machine Learning or Amazon SageMaker for instance.

          Should you compare with #GCP for instance, it's not quite there yet. Building around these managed services, #AWS allowed me to get my developers on a whole new level. Where they know what's under the hood. Where they know they have these services available and can build around them. Where they care and are responsible for operations and security and deployment of what they've worked on.

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          How developers use Amazon Machine Learning and NanoNets
          Avatar of Taylor Host
          Taylor Host uses Amazon Machine LearningAmazon Machine Learning

          Mild re-training data usage.

          How much does Amazon Machine Learning cost?
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