Sold by: MphasisÂ
Deployed on AWS
The solution tests and validates the input dataset sanity requirements with respect to Classification modeling
Overview
The solution explores the raw input dataset and identifies various data discrepancies like Data duplicates, Variable mix, Variables and Label drift. The solution tests for these discrepancies and report the user of its suitability for Classification Modeling. It output the quantified measure of the discrepancies and alert the user of necessary data preparation required prior to training.
Highlights
- Data sanity refers to the correctness of the data showing absence of discrepancies such as duplicates, single value columns, drifts, etc and its conformance to Machine learning requirements. This solution checks the input dataset for discrepancies like Data Duplicates, Single value column, Drifts, Variable mix, and gives an early warning to the user to perform data preparation before model training. The early identification of the data discrepancies avoids percolation of error to classification models and prediction
- The solution accepts tabular dataset containing at least one valid Label variable to be used in Classification modeling. The acceptable data types for variables include Numerical, Categorical and Strings. The solution gives the proportion of integer and strings mix within train test splits of the given dataset. The correctness of the dataset can be reported for various Label assignments (if more than one Label exist). The solution expects the user to define explicitly for Label, Index and Categorical variables in a dataset for every run.
- 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!
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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 |
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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
Version 1 of data sanity checks
Additional details
Inputs
- Summary
This solution takes input as zip file
- This file contains 3 files namely:
- "features_cat.txt" a. contains the column names of the variables that are to be treated as “Categorical” variables(case-sensitive)
- "index_label_names.csv"
- "input.csv" a. Exactly 1 field as "index_name" b. Exactly 1 field as "Lable" c. Columns "index_name" & "Lable" are case-sensitive d. The user has to input “index_name “and “Label” names in placeholder popped-up during run-time.
- This file contains 3 files namely:
- Input MIME type
- text/plain, application/zip, application/json
https://github.com/Mphasis-ML-Marketplace/Dataset-sanity-checks-for-Classification-/blob/main/Input/
https://github.com/Mphasis-ML-Marketplace/Dataset-sanity-checks-for-Classification-/blob/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 |
|---|---|---|---|
application/zip | This solution takes input as zip file
* This file contains 3 files namely:
- "features_cat.txt"
a. contains the column names of the variables that are to be treated as “Categorical” variables(case-sensitive)
- "index_label_names.csv"
- "input.csv"
a. Exactly 1 field as "index_name"
b. Exactly 1 field as "Lable"
c. Columns "index_name" & "Lable" are case-sensitive
d. The user has to input “index_name “and “Label” names in placeholder popped-up during run-time. | Type: FreeText | Yes |
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