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.
Feature Selection for Machine Learning
By:
Latest Version:
1.8
The solution runs user specified feature selection tasks on input data and provides relevant features as output.
Product Overview
The solution runs machine learning related feature selection operations on the input data. This will simplify the task of feature selection for a data scientist where the user will have to specify few selected parameters to generate the correct output instead of writing specific code for each individual feature selection tasks.
Key Data
Version
By
Type
Model Package
Highlights
This solution will provide the relevant and optimal features after running the user specified feature selection operations.
This solution saves a significant amount of time spent over developing and running different feature selection operations on the user data.
PACE - ML is Mphasis Framework and Methodology for end-to-end machine learning development and deployment. PACE-ML enables organizations to improve the quality & reliability of the machine learning solutions in production and helps automate, scale, and monitor them. Need customized Machine Learning and Deep Learning solutions? Get in touch!
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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$10.00/hr
running on ml.t2.medium
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 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 | $10.00 | |
ml.m5d.24xlarge | $10.00 | |
ml.m5.2xlarge | $10.00 | |
ml.c5d.4xlarge | $10.00 | |
ml.r5.12xlarge | $10.00 | |
ml.c4.2xlarge | $10.00 | |
ml.m4.10xlarge | $10.00 | |
ml.m5d.large | $10.00 | |
ml.m5d.4xlarge | $10.00 | |
ml.c4.4xlarge | $10.00 | |
ml.m5.xlarge | $10.00 | |
ml.c5.9xlarge | $10.00 | |
ml.m5d.12xlarge | $10.00 | |
ml.c4.large | $10.00 | |
ml.c4.8xlarge | $10.00 | |
ml.t2.large | $10.00 | |
ml.r5.2xlarge | $10.00 | |
ml.t2.2xlarge | $10.00 | |
ml.r5d.2xlarge | $10.00 | |
ml.m5.4xlarge | $10.00 | |
ml.c5d.large | $10.00 | |
ml.m4.16xlarge | $10.00 | |
ml.r5.large | $10.00 | |
ml.r5d.large | $10.00 | |
ml.m4.2xlarge | $10.00 | |
ml.r5d.12xlarge | $10.00 | |
ml.c5.2xlarge | $10.00 | |
ml.c5d.9xlarge | $10.00 | |
ml.r5.xlarge | $10.00 | |
ml.r5d.xlarge | $10.00 | |
ml.c4.xlarge | $10.00 | |
ml.m5.24xlarge | $10.00 | |
ml.m5d.xlarge | $10.00 | |
ml.c5.xlarge | $10.00 | |
ml.r5.24xlarge | $10.00 | |
ml.m5.12xlarge | $10.00 | |
ml.r5.4xlarge | $10.00 | |
ml.c5.large | $10.00 | |
ml.m4.xlarge | $10.00 | |
ml.c5.4xlarge | $10.00 | |
ml.m5d.2xlarge | $10.00 | |
ml.c5d.xlarge | $10.00 | |
ml.r5d.4xlarge | $10.00 | |
ml.m5.large | $10.00 | |
ml.t2.xlarge | $10.00 | |
ml.c5.18xlarge | $10.00 | |
ml.c5d.18xlarge | $10.00 | |
ml.t2.medium Vendor Recommended | $10.00 | |
ml.c5d.2xlarge | $10.00 |
Usage Information
Model input and output details
Input
Summary
This algorithm takes a zip file as an input. This zip file should contain exactly two files:
- Data.csv – this will be the data on which feature selection is to be done
- Config.json – This file should contain parameters specific to feature selection tasks to be executed on the supplied data. The parameters of this file are explained further below:
Input MIME type
application/zipSample input data
Output
Summary
The output will be the modified data in the form of a CSV file with the relevant features.
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
Feature Selection for Machine Learning
For any assistance reach out to us at:
AWS Infrastructure
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Learn MoreRefund Policy
Currently we do not support refunds, but you can cancel your subscription to the service at any time.
Customer Reviews
Anish Mahesh S.
View allFeature selection probably the most daunting task in ML journey
Jun 28, 2023
What do you like best about the product?Feature selection is probably the most daunting task in a ML
journey. Especially in an industrial setting there are a plethora of features which maybe higly
co-related to each other thus making the life of an industrial data scientist the most diffic... Read more
journey. Especially in an industrial setting there are a plethora of features which maybe higly
co-related to each other thus making the life of an industrial data scientist the most diffic... Read more
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