Sold by: MphasisÂ
Deployed on AWS
An explainable AI solution for providing global explanation for structured data models
Overview
The solution helps users interpret complex black-box machine learning models by bringing out the important features which the model uses for predictions. This can help the users to tweak/ modify the features to improve on models performance and help remove any biases that a particular feature can bring in, thus helping conform to any regulatory or compliance related requirements. It also provides dependence plots explaining relationship of the values of a feature to its corresponding feature importance.
Highlights
- This solution trains an explainer using the model and the train and test data provided. The explainer is then used to generate the global explanations in terms of the feature importance as well as dependence plots.
- This solution works with all models which can be pickled and implement a predict function. Dependence plot for any specific variable can also be generated.
- 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
Sold by
Delivery method
Latest version
Deployed on AWS
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Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
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
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)Â .
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.
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
Initial Release
Additional details
Inputs
- Summary
Input
- Supported content-types for inferencing: application/json
Input Schema: (For Training)
The Training requires three files to be present in S3 bucket:
- x_train.csv - This file contains the tabular data used to train model by the user
- model - model trained by user
- x_test.csv - This file contains the tabular data on which model is to tested for explanations
Input Schema: (For inferencing)
The inferencing require a json file with one or three keys:
- k - Top k features to be displayed in the graph. If only k is provided, for the top K features Dependence Plot would also be generated.
- feature1 - feature on the x-axis of Dependence plot. Should be provided if feature2 is provided
- feature2 - feature used to color the data points in Dependence plot. Should be provide if feature1 is provided.
Output
Content type: application/json. The json will be of a list containing image-uri's for the different plot. List size would depend upon the input provided. If only k is provided then list would be k+1 else of size 2.
Resource
- Input MIME type
- application/zip, text/csv, text/plain
See Input Summary
See Input Summary
Resources
Vendor resources
Support
Vendor support
For any assistance reach out to us at:
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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