
Overview
This solution will evaluate between several deep learning models of various architectures on the user provided data. It will identify the best performing deep learning model architecture on the basis of validation metric for text classification. This will reduce the time and effort for the model building task for a data scientist. This solution automates several of deep learning tasks in data science.
Highlights
- This solution will help identify the best deep learning model architecture for the text classification data set and help improve the turn-around time for any text processing development as well as save time of a data scientist.
- This solution can be used for various NLP based solutions like sentiment analysis , entity extraction etc. in various domains like banking , insurance , retail , legal and ecommerce.
- Mphasis DeepInsights is a cloud-based cognitive computing platform that offers data extraction & predictive analytics capabilities. Need Customized Deep learning and Machine Learning Solutions? Get in Touch!
Details
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Features and programs
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Pricing
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.2xlarge Inference (Batch) Recommended | Model inference on the ml.m5.2xlarge instance type, batch mode | $20.00 |
ml.m5.2xlarge Inference (Real-Time) Recommended | Model inference on the ml.m5.2xlarge instance type, real-time mode | $10.00 |
ml.m5.4xlarge Training Recommended | Algorithm training on the ml.m5.4xlarge instance type | $10.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $20.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $20.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $20.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $20.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $20.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $20.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $20.00 |
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.
Version release notes
Fixed Bugs
Additional details
Inputs
- Summary
This algorithm takes a zip file as an input. This zip file to be uploaded for training the model should follow the following file structure Sample.zip |----class 1 |----|----img1.png |----class 2 |----|----img1.png |----… |----class n |----|----img1.png |----dict.json
- Input MIME type
- text/csv, text/plain, application/zip
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
Train/test data | The zip file should contain images. The folder structure should be as specified in the usage instructions. Please go through them for more information. The current accepted image types are: png, jpg | Default value: 1
Type: Categorical
Allowed values: 1 | No |
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Customer reviews
Perfect AI technology for text analysing
It also helps to creat unique and hyperparameter data text from the sketch of data.
One of the best features is that it reduces human errors in the data as everything is automated and done with ML or AI tools for high data production.
You can use it to process and run the ongoing project; it adapts all the configurations of your systems and project information.
Modelling and labelling of data and text most uniquely and innovatively.
Does not support multiple file formats, which is not good. I think this should be improved in this software.
The algorithms are pretty complex to understand for the first time when using this software.