Sold by: MphasisÂ
Deployed on AWS
A sector agnostic GNN-based custom rules recommendation for device management using preset internal links for personalized automation.
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.
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.
- Mphasis DeepInsights is a cloud-based cognitive computing platform that offers data extraction & predictive analytics capabilities. Need customized Machine Learning and Deep Learning solutions? Get in touch!
Details
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Delivery method
Latest version
Deployed on AWS
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Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $8.00 |
ml.m5.xlarge Inference (Real-Time) Recommended | Model inference on the ml.m5.xlarge instance type, real-time mode | $8.00 |
ml.m5.xlarge Training Recommended | Algorithm training on the ml.m5.xlarge instance type | $16.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $8.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $8.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $8.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $8.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $8.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $8.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $8.00 |
Vendor refund policy
Currently we do not support refunds, but you can cancel your subscription to the service at any time.
Legal
Vendor terms and conditions
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Delivery details
Amazon SageMaker algorithm
An Amazon SageMaker algorithm is a machine learning model that requires your training data to make predictions. Use the included training algorithm to generate your unique model artifact. Then deploy the model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
This is version 1.1
Additional details
Inputs
- 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/csv
https://github.com/Mphasis-ML-Marketplace/Rule_recommendation_engine_for_automation/blob/main/input/training/input_zip.zip
https://github.com/Mphasis-ML-Marketplace/Rule_recommendation_engine_for_automation/blob/main/input/inference/test_sam.csv
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
train_rule.csv | this csv file must contain given below columns :
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.
| Type: Continuous | Yes |
train_devices.csv | this csv file must contain given below columns :
user_id: Identifier for the user.
device_id: ID of the device.
device_model: Model of the device | Type: Continuous | Yes |
Resources
Vendor resources
Support
Vendor support
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