Sold by: AgmisÂ
Overview
The model is used to monitor product stock levels on a store shelve. The model can identify if the particular product is present or if the shelve is empty. Together with stock management software, the model can be used to notify shop managers when stock on a shelve is running low and the shelve needs to be restocked.
Highlights
- Monitor product stock levels on a shop shelve.
- Notify when supplies are low and need to be restocked.
- Measure the item sell-out times and speed of restocking.
Details
Sold by
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Delivery method
Latest version
Deployed on AWS
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Features and programs
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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.xlarge Inference (Batch) Recommended | Model inference on the ml.m5.xlarge instance type, batch mode | $1.00 |
ml.m5.xlarge Inference (Real-Time) Recommended | Model inference on the ml.m5.xlarge instance type, real-time mode | $1.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $1.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $1.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $1.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $1.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $1.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $1.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $1.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $1.00 |
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We do not offer 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
Fixed issues with container security.
Additional details
Inputs
- Summary
This model can analyze images that are supplied as base64 string or stored in an Amazon S3 bucket.
- Input MIME type
- application/x-image
https://sagemaker-storage-data.s3.us-east-2.amazonaws.com/out-of-shelf/input/empty/franki-chamaki-wkvKZR4e2OI-unsplash.jpg-6.jpg
https://sagemaker-storage-data.s3.us-east-2.amazonaws.com/out-of-shelf/out-of-shelf-data.zip
Resources
Vendor resources
Support
Vendor support
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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