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.
Categorical Missing Data Imputation
By:
Latest Version:
3.0
Deep Learning based solution to impute missing data in categorical attributes of given structured data.
Product Overview
Categorical Missing Data Imputation is a robust deep learning based solution. This solution fills in missing values for categorical attributes by identifying data patterns in the input dataset. It helps reduce the data quality issues due to incomplete / non-available data.
Key Data
Version
By
Type
Model Package
Highlights
Deep Learning based algorithm helps in solving the issue of incomplete data by imputing the categorical missing values in a model-driven way.
The solution can handle the dataset with missing values in multiple variables. The solution is effective for data with up to 25% missing values in any of the variables. Solution minimizes the issues related to incomplete data and hence can be utilized as a first step in the data pre-processing for creating decision models.
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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$4.00/hr
running on ml.t2.medium
Model Batch Transform$8.00/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$0.056/host/hr
running on ml.t2.medium
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 | $4.00 | |
ml.m5.4xlarge | $4.00 | |
ml.m5.12xlarge | $4.00 | |
ml.m5d.24xlarge | $4.00 | |
ml.m5d.4xlarge | $4.00 | |
ml.m4.16xlarge | $4.00 | |
ml.m5.2xlarge | $4.00 | |
ml.m5.xlarge | $4.00 | |
ml.m4.xlarge | $4.00 | |
ml.m4.2xlarge | $4.00 | |
ml.m5d.2xlarge | $4.00 | |
ml.m5d.12xlarge | $4.00 | |
ml.m5.large | $4.00 | |
ml.t2.xlarge | $4.00 | |
ml.m4.10xlarge | $4.00 | |
ml.m5.24xlarge | $4.00 | |
ml.m5d.xlarge | $4.00 | |
ml.m5d.large | $4.00 | |
ml.t2.large | $4.00 | |
ml.t2.medium Vendor Recommended | $4.00 | |
ml.t2.2xlarge | $4.00 |
Usage Information
Fulfillment Methods
Amazon SageMaker
Input
- Supported content types:
text/csv- The solution can be used on numerical attributes in CSV format (UTF-8 encoded) containing missing values
- The input CSV should not contain fields with dates and textual data
- For better accuracy, please provide missing values not more than 25 percent in any variable
- Variables should have a minimum of 10 percent non-null values for imputation
- The input should not exceed 5000 rows and 15 columns with missing values
Output
- Content type:
text/csv - Output is in the form of ‘.csv’ file
- All the missing values will be imputed in their original positions in the original CSV
Invoking endpoint
AWS CLI Command
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 $model_name --body fileb://$file_name --content-type 'text/csv' --region us-east-2 output.csv
Resources
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
Categorical Missing Data Imputation
For any assistance reach out to us at:
AWS Infrastructure
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