Sold by: Mphasis
Deployed on AWS
An explainable AI solution based on tree based models providing global as well as local explanations
Overview
The solution helps users interpret complex black-box machine learning models by bringing out the important features which the model uses for predictions. It also identifies the features and their effect on the predictions, for each of the predictions. The solution supports 40+ tree based classifiers and regressors such as Random Forest, Decision Trees, XgBoost, CatBoost etc.
Highlights
- This solution trains an explainer using the tree based model. The explainer is then used to generate the global explanations in terms of the feature importance as well as dependence plots of top five features.
- The explainer also generates force plots along with a table of important features and their effect on the predictions.
- PACE - ML is Mphasis Framework and Methodology for end-to-end machine learning development and deployment. PACE-ML enables organizations to improve the quality & reliability of the machine learning solutions in production and helps automate, scale, and monitor them. Need customized Machine Learning and Deep Learning solutions? Get in touch!
Details
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Delivery method
Latest version
Deployed on AWS
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Pricing
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.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $16.00 |
ml.m5.large Inference (Real-Time) Recommended | Model inference on the ml.m5.large instance type, real-time mode | $8.00 |
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 | $16.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $16.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $16.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $16.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $16.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $16.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $16.00 |
Vendor refund policy
Currently we do not support refunds, but you can cancel your subscription to the service at any time.
Legal
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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
Bug Fixes and Performance Improvement
Additional details
Inputs
- Summary
Supported Algorithms
Click here to get the list of supported algorithms
Input
- Supported content-types for testing: application/zip
Input Schema: (For Training)
The Training requires three files to be present in S3 bukcet:
- matrix.csv - This file contains the sparse matrix used to train tree based model by the user
- model - Tree based model trained by user
- featureimportance.png - A blank file which will be replaced by an image with global explanations
- feature_names.csv - This file contains the list of features in the column 'features' Sample zipped files
Input Schema: (For Testing)
The Testing require three files to be ziped in input.zip file:
- matrix.csv - Same as mentioned for training
- feature_names.csv - Same as mentioned for training
- x_explain.csv - Initial record before preprocessing and creating the matrix for training
Output
Content type: application/json. The json will contain two fields:
- 'image-uri' - This field value is a image uri which user can copy-paste in broser url field to see the results for the model explainations
- 'factors' - Contains the factors effecting the prediction/classification by the model
Notebook
- Input MIME type
- application/zip
See Input Summary
See Input Summary
Resources
Vendor resources
Support
Vendor support
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AWS infrastructure support
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