Amazon Sagemaker
Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.
Explainable AI: Structured Data Models
By:
Latest Version:
1.0
An explainable AI solution for providing global explanation for structured data models
Product 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.
Key Data
Version
By
Type
Algorithm
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.
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Pricing Information
Use this tool to estimate the software and infrastructure costs based your configuration choices. Your usage and costs might be different from this estimate. They will be reflected on your monthly AWS billing reports.
Contact us to request contract pricing for this product.
Estimating your costs
Choose your region and launch option to see the pricing details. Then, modify the estimated price by choosing different instance types.
Version
Region
Software Pricing
Algorithm Training$10/hr
running on ml.m5.large
Model Realtime Inference$8.00/hr
running on ml.m5.large
Model Batch Transform$16.00/hr
running on ml.m5.large
Infrastructure PricingWith Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
With Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
SageMaker Algorithm Training$0.115/host/hr
running on ml.m5.large
SageMaker Realtime Inference$0.115/host/hr
running on ml.m5.large
SageMaker Batch Transform$0.115/host/hr
running on ml.m5.large
Algorithm Training
For algorithm training in Amazon SageMaker, the software is priced based on hourly pricing that can vary by instance type. Additional infrastructure cost, taxes or fees may apply.InstanceType | Algorithm/hr | |
|---|---|---|
ml.m4.4xlarge | $10.00 | |
ml.m5.4xlarge | $10.00 | |
ml.m4.16xlarge | $10.00 | |
ml.m5.2xlarge | $10.00 | |
ml.p3.16xlarge | $10.00 | |
ml.m4.2xlarge | $10.00 | |
ml.c5.2xlarge | $10.00 | |
ml.p3.2xlarge | $10.00 | |
ml.c4.2xlarge | $10.00 | |
ml.m4.10xlarge | $10.00 | |
ml.c4.xlarge | $10.00 | |
ml.m5.24xlarge | $10.00 | |
ml.c5.xlarge | $10.00 | |
ml.p2.xlarge | $10.00 | |
ml.m5.12xlarge | $10.00 | |
ml.p2.16xlarge | $10.00 | |
ml.c4.4xlarge | $10.00 | |
ml.m5.xlarge | $10.00 | |
ml.c5.9xlarge | $10.00 | |
ml.m4.xlarge | $10.00 | |
ml.c5.4xlarge | $10.00 | |
ml.p3.8xlarge | $10.00 | |
ml.m5.large Vendor Recommended | $10.00 | |
ml.c4.8xlarge | $10.00 | |
ml.p2.8xlarge | $10.00 | |
ml.c5.18xlarge | $10.00 |
Usage Information
Training
See Input Summary
Channel specification
Fields marked with * are required
training
*Input modes: File
Content types: application/zip, text/plain, application/json, text/csv
Compression types: None
Model input and output details
Input
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/plainSample input data
See Input SummaryOutput
Summary
See Input Summary
Output MIME type
application/json, text/plain, text/csvSample output data
See Input SummarySample notebook
Additional Resources
End User License Agreement
By subscribing to this product you agree to terms and conditions outlined in the product End user License Agreement (EULA)
Support Information
Explainable AI: Structured Data Models
For any assistance reach out to us at: https://www2.mphasis.com/AWS-Marketplace-Support-LP.html
AWS Infrastructure
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Learn MoreRefund Policy
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