Sold by: Mphasis
Deployed on AWS
The solution measures the prominent robustness metrics for a Keras based classifier for tabular data classification
Overview
This solution measures the key robustness measures for the Keras based tabular data classification model. Robust models are immune to the adversarial attacks, thus measuring robustness of the models is important from the point of view of data and information security. This solution leverages four metrics that reflects robustness of the model.
Highlights
- Machine learning model run with a risk of adversarial attacks which can compromise model effectiveness and/or data and information security. The solution simulates these attacks to measure the robustness of the model. Here in this solution, Fastest Gradient Sign Method(FGSM) has been used to measure four robustness metrics that explain the immunity of the model to the adversarial attacks.
- Solution provides four metrics as output. 1. Empirical robustness 2. Loss sensitivity 3. CLEVER Score 4. Difference in accuracy. These four metrics gives the broad overview about the robustness of the model and sensitivity of the Keras classifier to the adversarial attacks.
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Latest version
Deployed on AWS
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Pricing
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Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $0.00 |
ml.m5.large Inference (Real-Time) Recommended | Model inference on the ml.m5.large instance type, real-time mode | $0.00 |
ml.m5.large Training Recommended | Algorithm training on the ml.m5.large instance type | $10.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $0.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $0.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $0.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $0.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $0.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $0.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $0.00 |
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Delivery details
Amazon SageMaker algorithm
An Amazon SageMaker algorithm is a machine learning model that requires your training data to make predictions. Use the included training algorithm to generate your unique model artifact. Then deploy the model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
This is the first version.
Additional details
Inputs
- Summary
The solution requires the following input files:
- a “train.csv” file containing all features with label with which you have trained the model (can even be a sample of train data).
- a “test.csv” file containing all features with label with which we have to test the model.
- a “model.h5” file which is the trained ART Keras classifier model.
- a “eps.json” file containing the dictionary of epsilon(perturbation) value.
Refer Input Data Descriptions for the required format of each file.
- Limitations for input type
- Inferencing is done within training pipeline itself. Additional Real time inference endpoint/batch transform job is not required.
- Input MIME type
- application/zip
https://github.com/Mphasis-ML-Marketplace/Robustness-Metrics-for-Tabular-data/tree/main/Input
https://github.com/Mphasis-ML-Marketplace/Robustness-Metrics-for-Tabular-data/tree/main/Input
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
train.csv | 1) Exactly one column to be defined by user as “label”
2) Other columns should have feature values (Column names can be anything) | Type: Continuous | Yes |
test.csv | 1) Exactly one column to be defined by user as “label”
2) Other columns should have feature values (Column names can be anything) | Type: Continuous | Yes |
model.h5 | Trained ART Keras classifier that must be stored in .h5 format and use softmax activation to produce probabilistic output. | Type: FreeText | Yes |
eps.json | Exactly one dictionary with one key named ‘eps' and value to be a numeric value between 0 and 1 (eps value is the perturbation that we need to provide for FGSM attack-refer ART documentation) | Type: Continuous | Yes |
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