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 for Model Selection
By:
Latest Version:
1.6
This is an AutoML solution which runs multiple ML models on the user data and selects the best model.
Product Overview
This AutoML solution runs several classification and regression machine learning models on the input data. It will identify the best performing model based on the user specified evaluation metric. This will simplify the task of model building for a data scientist where the user will have to specify few selected parameters to find the best model for the data set.
Key Data
Version
By
Type
Model Package
Highlights
This solution will help identify the best machine learning model for the data set given the evaluation metric.
This solution saves a significant amount of time spent over developing and running different preprocessing 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 algorithm will run its tasks.
- Config.json – This file should contain parameters specific to algorithm to execute tasks on the supplied data. The available parameter are as follows with their available values:
Input MIME type
application/zipSample input data
Output
Summary
The output will be a score grid containing all the models executed.
Output MIME type
text/csvSample output data
Sample notebook
Additional Resources
End User License Agreement
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Support Information
AutoML for Model Selection
For any assistance reach out to us at:
AWS Infrastructure
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Learn MoreRefund Policy
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