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.
Rule Recommendation Model for Automation
By:
Latest Version:
1.0
A sector agnostic GNN-based custom rules recommendation for device management using preset internal links for personalized automation.
Product Overview
Our GNN-based Automation Rule Recommendation Model is a sector agnostic solution designed to optimize automation systems for smart homes, agriculture, healthcare, retail, logistics and others. Define specific conditions and actions for a set of interconnected devices to simplify managing multiple devices and tailors their operations to specific needs, personalization, and improves overall efficiency. Leveraging user-specific data on existing devices and the pre-defined automation rules, the model predicts and recommends new rules to align with unique user behaviors and usage patterns. The model empowers the users to manage the complexity of interconnected devices and ensures that the operation is tailored, helpful, and convenient for the users. The model is pre-built with a pipeline to allow for continuous training with user data, enabling it to adapt and improve to meet evolving needs and preferences, offering a long-term solution for efficient device management.
Key Data
Version
By
Type
Algorithm
Highlights
Users can train the model with their own Rule data, allowing for tailored recommendations that fit their specific . This model can be trained for any set of devices, ensuring flexibility and applicability across a wide range of setups.
The system accepts custom rule data for training, enabling personalized recommendations aligned with specific needs. This adaptable model accommodates any device configuration, ensuring versatility across diverse setups.
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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$16/hr
running on ml.m5.xlarge
Model Realtime Inference$8.00/hr
running on ml.m5.xlarge
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 Algorithm Training$0.23/host/hr
running on ml.m5.xlarge
SageMaker Realtime Inference$0.23/host/hr
running on ml.m5.xlarge
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 | $16.00 | |
ml.c5n.18xlarge | $16.00 | |
ml.g4dn.4xlarge | $16.00 | |
ml.m5.4xlarge | $16.00 | |
ml.m4.16xlarge | $16.00 | |
ml.m5.2xlarge | $16.00 | |
ml.p3.16xlarge | $16.00 | |
ml.g4dn.2xlarge | $16.00 | |
ml.c5n.xlarge | $16.00 | |
ml.m4.2xlarge | $16.00 | |
ml.c5.2xlarge | $16.00 | |
ml.p3.2xlarge | $16.00 | |
ml.c4.2xlarge | $16.00 | |
ml.g4dn.12xlarge | $16.00 | |
ml.p4d.24xlarge | $16.00 | |
ml.m4.10xlarge | $16.00 | |
ml.c4.xlarge | $16.00 | |
ml.m5.24xlarge | $16.00 | |
ml.c5.xlarge | $16.00 | |
ml.g4dn.xlarge | $16.00 | |
ml.p2.xlarge | $16.00 | |
ml.m5.12xlarge | $16.00 | |
ml.g4dn.16xlarge | $16.00 | |
ml.p2.16xlarge | $16.00 | |
ml.c4.4xlarge | $16.00 | |
ml.m5.xlarge Vendor Recommended | $16.00 | |
ml.c5.9xlarge | $16.00 | |
ml.m4.xlarge | $16.00 | |
ml.c5.4xlarge | $16.00 | |
ml.p3.8xlarge | $16.00 | |
ml.m5.large | $16.00 | |
ml.c4.8xlarge | $16.00 | |
ml.c5n.2xlarge | $16.00 | |
ml.p2.8xlarge | $16.00 | |
ml.g4dn.8xlarge | $16.00 | |
ml.c5n.9xlarge | $16.00 | |
ml.c5.18xlarge | $16.00 | |
ml.c5n.4xlarge | $16.00 |
Usage Information
Training
The user needs two csv files:
- train_rule.csv containing details aboutthe existing rules including userid, Trigger device, Trigger deviceid Trigger state, Trigger state id, Action action id, Action Device, Action Device id and rule.
- train_devices.csv containing all the devices with user id and device id
Channel specification
Fields marked with * are required
training
*Input modes: File
Content types: application/zip, application/gzip, text/csv, application/json
Compression types: None
Model input and output details
Input
Summary
The user needs two csv files:
train_rule.csv containing details aboutthe existing rules including userid, Trigger device, Trigger deviceid Trigger state, Trigger state id,Action action id,Action Device, Action Device id and rule
train_devices.csv containing all the devices with user id and device id
Limitations for input type
1. Input should be in zip format and name should be input_zip.zip.
2. input_zip.zip should contain 2 .csv files. Name of the csv files should be train_rule.csv and train_devices.csv.
Input MIME type
text/csvSample input data
Output
Summary
Out put file will have same columns as train_rule containing user_id: Identifier for the user. trigger_device: Trigger device (e.g., Camera). trigger_device_id: ID of the trigger device. trigger_state: State of the trigger device. trigger_state_id: ID of the trigger state. action: Action to be taken. action_id: ID of the action. action_device: Device performing the action. action_device_id: ID of the action device. rule: format: trigger_device_id_trigger_state_id_action_id_action_device_id
Output MIME type
text/csvSample output data
Sample notebook
Additional Resources
End User License Agreement
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Support Information
Rule Recommendation Model for Automation
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