Amazon Sagemaker
Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.
AutoML: Text Classification
By:
Latest Version:
1.0
This is a NLP based solution that can be leveraged for any text classification problem to get the best model through AutoML training.
Product 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.
Key Data
Version
By
Type
Model Package
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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Pricing Information
Use this tool to estimate the software and infrastructure costs based your configuration choices. Your usage and costs might be different from this estimate. They will be reflected on your monthly AWS billing reports.
Contact us to request contract pricing for this product.
Estimating your costs
Choose your region and launch option to see the pricing details. Then, modify the estimated price by choosing different instance types.
Version
Region
Software Pricing
Model Realtime Inference$8.00/hr
running on ml.m5.large
Model Batch Transform$16.00/hr
running on ml.m5.large
Infrastructure PricingWith Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
With Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
SageMaker Realtime Inference$0.115/host/hr
running on ml.m5.large
SageMaker Batch Transform$0.115/host/hr
running on ml.m5.large
Model Realtime Inference
For model deployment as Real-time endpoint in Amazon SageMaker, the software is priced based on hourly pricing that can vary by instance type. Additional infrastructure cost, taxes or fees may apply.InstanceType | Realtime Inference/hr | |
|---|---|---|
ml.m4.4xlarge | $8.00 | |
ml.m5.4xlarge | $8.00 | |
ml.m4.16xlarge | $8.00 | |
ml.m5.2xlarge | $8.00 | |
ml.p3.16xlarge | $8.00 | |
ml.m4.2xlarge | $8.00 | |
ml.c5.2xlarge | $8.00 | |
ml.p3.2xlarge | $8.00 | |
ml.c4.2xlarge | $8.00 | |
ml.m4.10xlarge | $8.00 | |
ml.c4.xlarge | $8.00 | |
ml.m5.24xlarge | $8.00 | |
ml.c5.xlarge | $8.00 | |
ml.p2.xlarge | $8.00 | |
ml.m5.12xlarge | $8.00 | |
ml.p2.16xlarge | $8.00 | |
ml.c4.4xlarge | $8.00 | |
ml.m5.xlarge | $8.00 | |
ml.c5.9xlarge | $8.00 | |
ml.m4.xlarge | $8.00 | |
ml.c5.4xlarge | $8.00 | |
ml.p3.8xlarge | $8.00 | |
ml.m5.large Vendor Recommended | $8.00 | |
ml.c4.8xlarge | $8.00 | |
ml.p2.8xlarge | $8.00 | |
ml.c5.18xlarge | $8.00 |
Usage Information
Model input and output details
Input
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/zipSample input data
Output
Summary
Output csv would contain different models with their performance metrics and hyperparameters
Output MIME type
text/csvSample output data
Sample notebook
Additional Resources
End User License Agreement
By subscribing to this product you agree to terms and conditions outlined in the product End user License Agreement (EULA)
Support Information
AutoML: Text Classification
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