Overview
How green is your car? Cars running on hyrocarbon based fuels are a major contributor of greenhouse gasses in the atmosphere. With the sustainability becoming a decision criteria of acquiring a car along with the other usual attributes like performance, asthetics, reliability etc, this solution provides a simple way of ascertaining the running carbon footprint of the car. The solution uses supervised machine learning approch to predict the carbon dioxide (CO2) emission in kg per mile of running of a vehicle in standard conditions. The solution uses vehicle's dimensions, transmission, manufactured year, gears, horsepower, drive etc. to predict the fuel consumption and the carbon emission of the vehicle.
Highlights
- The machine learning solution can analyze various car attributes like engine size, fuel type, vehicle weight, and more to provide precise predictions of the car's carbon footprint and mileage. This helps car owners and potential buyers make informed decisions based on the environmental impact and fuel efficiency of the vehicle.
- The machine learning solution can be integrated with various platforms and tools, making it easy for users to access and understand the predictions. This may include interactive dashboards, mobile applications, or integration with car dealership websites, allowing users to visualize and compare the carbon footprint and mileage of different vehicles easily.
- Need more machine learning, deep learning, NLP and Quantum Computing solutions. Reach out to us at Harman DTS.
Details
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Delivery method
Latest version
Deployed on AWS
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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 | $10.00 |
ml.t2.medium Inference (Real-Time) Recommended | Model inference on the ml.t2.medium instance type, real-time mode | $5.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $10.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $10.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $10.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $10.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $10.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $10.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $10.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $10.00 |
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We do not provide any usage related refunds at this time.
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a 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
Bug fixes and feature updates
Additional details
Inputs
- Summary
The input data must be in json format.
- Input MIME type
- application/json
https://github.com/HDTS-user/lifeware-automobile-carbon-footprint/blob/main/input/test.json
https://github.com/HDTS-user/lifeware-automobile-carbon-footprint/blob/main/input/test.json
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
Dimensions.Height | Height of the car in cm | Type: Integer | Yes |
Dimensions.Length | Length of the car in cm | Type: Integer | Yes |
Dimensions.Width | Width of the car in cm | Type: Integer | Yes |
Gears | Number of Gears | Type: Integer | Yes |
Transmission | The full name of this type of transmission, based on its Classification and number of forward gears. | Type: Categorical
Allowed values: 4 Speed Automatic, 4 Speed Automatic Select Shift, 4 Speed Manual, - 4 can be replaced with values upto 8. | Yes |
Fuel_Type | Whether this car takes "Gasoline", "Diesel fuel", "Electricity", "Compressed natural gas", or "E85". | Type: Categorical
Allowed values: "Gasoline", "Diesel fuel", "Electricity", "Compressed natural gas", "E85". | Yes |
Identification.Year | The year that this car was released. | Type: Integer | Yes |
Horsepower | A measure of the engine's power. A unit of power equal to 550 foot-pounds per second (745.7 watts). | Type: Integer | Yes |
Torque | The torque of the engine, measured in lb/ft. When an engine is said to make "200 lb-ft of torque", it means that 200 pounds of force on a 1-foot lever is needed to stop its motion. | Type: Integer | Yes |
Drive | Which wheels get the engine power | Type: Categorical
Allowed values: "Rear-wheel drive", "All-wheel drive", "Four-wheel drive", "Front-wheel drive" | Yes |
Resources
Vendor resources
Support
Vendor support
Business hours email support marketplaceSupp@harman.comÂ
AWS infrastructure support
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