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Amazon Machine Learning
Amazon Machine Learning

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Amazon Machine Learning vs Paperspace: What are the differences?

Amazon Machine Learning: 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; Paperspace: The way to access and manage limitless computing power in the cloud. It is a high-performance cloud computing and ML development platform for building, training and deploying machine learning models. Tens of thousands of individuals, startups and enterprises use it to iterate faster and collaborate on intelligent, real-time prediction engines.

Amazon Machine Learning and Paperspace can be primarily classified as "Machine Learning as a Service" tools.

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, Paperspace provides the following key features:

  • Intelligent alert
  • Two-factor authentication
  • Share drives
- 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 Paperspace?

It is a high-performance cloud computing and ML development platform for building, training and deploying machine learning models. Tens of thousands of individuals, startups and enterprises use it to iterate faster and collaborate on intelligent, real-time prediction engines.
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              What are some alternatives to Amazon Machine Learning and Paperspace?
              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.
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              Decisions about Amazon Machine Learning and Paperspace
              Julien DeFrance
              Julien DeFrance
              Principal Software Engineer at Tophatter | 2 upvotes 16.1K views
              atSmartZipSmartZip
              Serverless
              Serverless
              AWS Lambda
              AWS Lambda
              Amazon Machine Learning
              Amazon Machine Learning
              Amazon SageMaker
              Amazon SageMaker
              #PaaS
              #GCP
              #FaaS

              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 Paperspace
              Avatar of Taylor Host
              Taylor Host uses Amazon Machine LearningAmazon Machine Learning

              Mild re-training data usage.

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