Sold by: MphasisÂ
Deployed on AWS
This is a NLP based solution that can be leveraged for any text classification problem to get the best model through AutoML training.
Overview
This solution provides an AutoML piepline which takes in an input csv that contains the labelled text data for any usecase along with a JSON file that contains the desired preprocessing steps that need to be used on the text data. The preprocessed data is then vectorized using different embeddings. Based on the train/test split in the input JSON, multiple ML models are trained using the various embeddings using the concept of AutoML. Once the models are trained and tuned, a leaderboard is returned to the user. The leaderboard contains the models sorted based on the chosen metric, all the metrics relevant to the models, and hyperparameter and embeddings used in the respective models. This will help the user in determining the best model for the text classification task at hand and the chosen model can be replicated or fine-tuned further in the future.
Highlights
- This solution is tested on various text classfication problems both binary as well as multiclass classification. The solution extracts the text columns and the preprocessing techniques from JSON input provided by the user, thus making the methodology flexible for any text classification usecase.
- This solution can be used by individuals and companies in sectors like e-commerce, manufacturing, retail, etc. to perform text classification on data with both binary or multi-class labels. Some examples include categorizing email as spam or not, classifying the text reviews of different products or bucketing reviews into different sentiments.
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Details
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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.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.
Legal
Vendor terms and conditions
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Content disclaimer
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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
Following are the mandatory inputs guidelines:
- The input zip file should have csv file and json file (consisting of user inputs)
- Input csv must be having two columns named - "target" and "text"
- Input zip file must be named as "Input.zip"
- Supported content types: application/zip.
- Input MIME type
- application/zip
https://github.com/Mphasis-ML-Marketplace/AutoML-Text-Classification/tree/main/Input
https://github.com/Mphasis-ML-Marketplace/AutoML-Text-Classification/tree/main/Input
Resources
Vendor resources
Support
Vendor support
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