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.
Active Learning for Text Classification
By:
Latest Version:
1.4
This algorithm trains a supervised text classification model and uses Active Learning to provide the most relevant samples for tagging
Product Overview
Active Learning for Text Classification trains a text classification model using a small corpus of training data and provides the most appropriate samples from a huge corpus of unlabeled data to be annotated in order to improve the model accuracy significantly. Using Active Learning this algorithm helps in identifying the most effective data sample to be tagged first thus reducing the time and effort to build a usable Machine learning model.
Key Data
Version
By
Type
Algorithm
Highlights
Active Learning for Text Classification can be used for prioritizing the data labeling task and thereby drastically reduce the data tagging effort required to build a working Machine Learning model.
This solution can be used to iteratively sample the right data points & train the Machine Learning model to build a supervised machine learning algorithm. It helps in identifying which samples to label first based on the rules learned by Machine Learning model. It uses Active Learning methodologies to select the right sample data points from unlabeled data to build a better performing machine learning model much faster.
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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
Algorithm Training$10/hr
running on ml.m5.4xlarge
Model Realtime Inference$10.00/hr
running on ml.m5.large
Model Batch Transform$20.00/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 Algorithm Training$0.922/host/hr
running on ml.m5.4xlarge
SageMaker Realtime Inference$0.115/host/hr
running on ml.m5.large
SageMaker Batch Transform$0.115/host/hr
running on ml.m5.large
Algorithm Training
For algorithm training 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 | Algorithm/hr | |
|---|---|---|
ml.m4.4xlarge | $10.00 | |
ml.m5.4xlarge Vendor Recommended | $10.00 | |
ml.m4.16xlarge | $10.00 | |
ml.m5.2xlarge | $10.00 | |
ml.p3.16xlarge | $10.00 | |
ml.m4.2xlarge | $10.00 | |
ml.c5.2xlarge | $10.00 | |
ml.p3.2xlarge | $10.00 | |
ml.c4.2xlarge | $10.00 | |
ml.m4.10xlarge | $10.00 | |
ml.c4.xlarge | $10.00 | |
ml.m5.24xlarge | $10.00 | |
ml.c5.xlarge | $10.00 | |
ml.p2.xlarge | $10.00 | |
ml.m5.12xlarge | $10.00 | |
ml.p2.16xlarge | $10.00 | |
ml.c4.4xlarge | $10.00 | |
ml.m5.xlarge | $10.00 | |
ml.c5.9xlarge | $10.00 | |
ml.m4.xlarge | $10.00 | |
ml.c5.4xlarge | $10.00 | |
ml.p3.8xlarge | $10.00 | |
ml.m5.large | $10.00 | |
ml.c4.8xlarge | $10.00 | |
ml.p2.8xlarge | $10.00 | |
ml.c5.18xlarge | $10.00 |
Usage Information
Training
- The system trains on user provided text datasets.
- The train dataset must contain 2 files - "train.csv" and "validation.csv" with 'utf-8' encoding.
- train.csv
- train.csv must contain 3 columns - ID, Text and Category.
- validation.csv
The format of validation.csv is similar to train.csv and must contain 3 columns - ID, Text and Category.
ID: unique identification number associated with the text
Text: Textual data that needs to be categorized.
Category: Class with the associated text.
Please refer sample notebook for detailed description.
Channel specification
Fields marked with * are required
training
*Input modes: File
Content types: application/zip, text/plain, application/json, text/csv
Compression types: None
Model input and output details
Input
Summary
If you are using real time inferencing, please create the endpoint first and then use the following command to invoke it:
aws sagemaker-runtime invoke-endpoint --endpoint-name "endpoint-name" --body fileb://$file_name --content-type text/csv --accept application/output.csvInput MIME type
text/csvSample input data
Output
Summary
- Content types: text/csv
- The output will be a csv file with sampled data points from unlablled data. The output csv file will contain ID and the associated text sampled for human annotation.
- These sampled datapoints should be annotated, added to train.csv and removed from unlablled.csv before training the machine learning model again.
Output MIME type
text/plain, text/csv, application/jsonSample output data
Sample notebook
Additional Resources
End User License Agreement
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Support Information
Active Learning for Text Classification
For any assistance reach out to us at:
AWS Infrastructure
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