Overview
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
Typical total price
$0.15/hour
Pricing
- ...
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
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Content disclaimer
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:
- Create an instance using the AMI
- Connect to the instance you created with any method you want (default user name is "ec2-user")
- AFE application is a Python library and already installed in a virtual environment named orgenv on the machine.
- Execute sample script: python3.9 afe.py
- Outputs can be reached from the output_folder location which is set in output_settings
Custom execution:
- Transfer the data you want to run the AFE application to the machine
- Create a python script, or import it to the machine
- Import AFE library: from organon_afe.afe.services.afe_executor import AFE
- 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
- 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
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Support
Vendor 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.