Sold by: MphasisÂ
Deployed on AWS
A solution to identify and quantify data drift in categorical variables between multiple datasets.
Overview
Machine Learning model performance degrades over time and the drift associated with data is one of the main reasons for the gradual drop in model accuracy. Drift Detection in Categorical variables aids in finding the extent of drift observed in data with respect to a reference dataset. The solution helps in identifying the features causing model’s accuracy loss and choosing a suitable interval to retrain the models.
Highlights
- This solution enables users to pinpoint the categorical features causing degradation in model performance. This can be leveraged to find out the features with drift between the training data and the production data.
- This solution uses multiple statistical metrics to measure the distance between reference data and evaluation data.
- 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.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 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge 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.
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a 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
Input:
Following are the mandatory inputs guidelines: • The algorithm works with two CSVs having categorical features with datapoints not more than 3.8millions. • Two csv must be named “Reference_data.csv” and “Drifted_data.csv". • Supported content types: application/zip.
Output:
Instructions for output interpretation: • Output will be the instances at which drift has occurred. • Output content type: A “.csv” file with the instances. • Supported content types: 'text/csv'.
Invoking endpoint:
If you are using real time inferencing, please create the endpoint first and then use the following command to invoke it:: aws sagemaker-runtime invoke-endpoint --endpoint-name "endpoint-name" --body fileb://sample.csv --content-type text/csv --accept text/csv out.csv
Resources:
- Input MIME type
- application/zip
See Input Summary
See Input Summary
Resources
Vendor resources
Support
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