Sold by: MphasisÂ
Deployed on AWS
Explainable AI solution to extract features which explain deep learning model predictions.
Overview
The solution applies deep learning model (CNN) to classify text data such as reviews and transcripts to identify features leading to prediction of user defined classes. It has explainable AI functionality which helps to understand why the model predicts the class based on key words and phrases in the text. The solution is adaptable and can be trained on any textual dataset containing user defined classes.
Highlights
- This deep learning and explainable AI solution trains an explainer using a CNN model. The explainer is then used to generate the local explanations in terms of word importance as well as provide a visual representation highlighting the keywords. The model is trainable on customer provided data and supports all NLP models that are trained on CNN.
- This solution can be applied across industries for users seeking to incorporate model explainability and convert black box models into simpler interpretable models.
- 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.xlarge Inference (Batch) Recommended | Model inference on the ml.m5.xlarge instance type, batch mode | $16.00 |
ml.m5.xlarge Inference (Real-Time) Recommended | Model inference on the ml.m5.xlarge instance type, real-time mode | $8.00 |
ml.m5.xlarge Training Recommended | Algorithm training on the ml.m5.xlarge 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.
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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
The deployed solution has these 2 steps: • The system trains on user provided textual data and builds & saves a deep learning model • Once the model is generated, the solution can be used to predict the label present in any input textual data To achieve this end, the solution deploys the following 2 APIs over AWS Sagemaker:
1.Training API: The solution requires two CSV file as input containing textual data, labels and model related information 2.Testing API: The solution requires one CSV file containing textual data and model information
Input
• Supported content types: text/csv • The algorithm works with textual data. ** Following are the mandatory inputs for the Training API:**
• Sample input file: (https://tinyurl.com/y6nuecd6 ) • The input must be provided as 2 separate CSV files • First file must be instructions to create a model in CSV format, with following columns - 'name', 'datacol', 'labelcol', 'epochs'. This file can have multiple rows to train multiple models. (max=5) • name – Name of model to be created • datacol – Column name for textual data in training dataset
• labelcol – Column name for label in training dataset • epochs – Number of epochs to be run for the model
• Second file must be training data, It must have one column for textual data & at least one column of labels for the model to be trained. Maximum label columns allowed is 5.
• Label and Data columns must match in both the files. • The solution currently handles only English language text with a maximum of 1000 character per row of textual data and maximum 10000 rows.** Following are the mandatory inputs for the Testing API:**
• The input must be provided as CSV file • Sample input file: (https://tinyurl.com/y2e8occw ) • Input file must contain the following columns 'review' & 'feature', where 'review' column will contain the textual data and 'feature' column will contain name of the model which was used for prediction
Output
• Content type: text/csv • Sample output file:(https://tinyurl.com/y6xtfo9l ) • The first column (review) has the textual data for prediction • The second column (feature) has the name of the model which was used for prediction • The third column (result) contains the results predicted by testing API
Resources
- Input MIME type
- application/zip, text/csv, text/plain
See Input Summary
See Input Summary
Resources
Vendor resources
Support
Vendor support
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