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    Autonon Automated Feature Extraction

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    AWS Free Tier
    Automated Feature Extraction (AFE) extracts and selects an optimal set of predictive features from transactional datasets without human effort. AFE is part of Autonon AutoML solution created by Organon Analytics.
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    Autonon Automated Feature Extraction

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    Overview

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    Automated Feature Extraction (AFE) enables the creation of automated and supervised extraction of an optimal set of predictive features from transactional datasets without human effort. It helps to transform temporal and relational datasets into feature matrices for modeling. The user just needs to fill in a few configurations about the raw data source. Then, AFE generates a lake of input features from transactional tables. Although the input lake may be large at the level of millions of variables, AFE intelligently benefits from supervised and unsupervised methods to reduce the scale of the lake to the thousands. The fact that AFE generates features directly from transactional tables without any data preprocessing is a feature that other AutoML products do not have. AFE makes Autonon AutoML a unique solution created by Organon Analytics.

    Highlights

    • Automated feature extraction from raw data
    • Transformation of temporal raw datasets
    • Supervised and unsupervised feature selection

    Details

    Delivery method

    Delivery option
    64-bit (x86) Amazon Machine Image (AMI)

    Latest version

    Operating system
    AmazonLinux 5.10

    Typical total price

    This estimate is based on use of the seller's recommended configuration (t3a.xlarge) in the US East (N. Virginia) Region. View pricing details

    $0.15/hour

    Pricing

    Autonon Automated Feature Extraction

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    Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
    Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator  to estimate your infrastructure costs.

    Usage costs (139)

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    • ...
    Instance type
    Product cost/hour
    EC2 cost/hour
    Total/hour
    t2.nano
    $0.00
    $0.006
    $0.006
    t2.micro
    AWS Free Tier
    $0.00
    $0.012
    $0.012
    t2.small
    $0.00
    $0.023
    $0.023
    t2.medium
    $0.00
    $0.046
    $0.046
    t2.large
    $0.00
    $0.093
    $0.093
    t2.xlarge
    $0.00
    $0.186
    $0.186
    t2.2xlarge
    $0.00
    $0.371
    $0.371
    t3.nano
    $0.00
    $0.005
    $0.005
    t3.micro
    AWS Free Tier
    $0.00
    $0.01
    $0.01
    t3.small
    $0.00
    $0.021
    $0.021

    Additional AWS infrastructure costs

    Type
    Cost
    EBS General Purpose SSD (gp2) volumes
    $0.10/per GB/month of provisioned storage

    Vendor refund policy

    No Refunds

    Legal

    Vendor terms and conditions

    Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA) .

    Content disclaimer

    Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.

    Usage information

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    Delivery details

    64-bit (x86) Amazon Machine Image (AMI)

    Amazon Machine Image (AMI)

    An AMI is a virtual image that provides the information required to launch an instance. Amazon EC2 (Elastic Compute Cloud) instances are virtual servers on which you can run your applications and workloads, offering varying combinations of CPU, memory, storage, and networking resources. You can launch as many instances from as many different AMIs as you need.

    Version release notes

    First release

    Additional details

    Usage instructions

    Quick start:

    1. Create an instance using the AMI
    2. Connect to the instance you created with any method you want (default user name is "ec2-user")
    3. AFE application is a Python library and already installed in a virtual environment named orgenv on the machine.
    4. Execute sample script: python3.9 afe.py
    5. Outputs can be reached from the output_folder location which is set in output_settings

    Custom execution:

    1. Transfer the data you want to run the AFE application to the machine
    2. Create a python script, or import it to the machine
    3. Import AFE library: from organon_afe.afe.services.afe_executor import AFE
    4. Write your script using the AFE library. To see how to use AFE, check the documentation: https://organonanalytics.atlassian.net/wiki/spaces/AOT/pages/2096267279/AFE+User+Manual 
    5. Use Python 3.9 for execution: python3.9 your_script.py

    Notes: afe.py and test data are given for a sample execution Use pip3.9 for additional installs to the environment There are no system credentials or cryptographic keys in the product, and no data that needs to be decrypted. Data sharing is the user's responsibility.

    Resources

    Vendor resources

    Support

    AWS infrastructure support

    AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.

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