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.
Retail Store Product Demand Forecasting Free trial
By:
Latest Version:
1.0
Generate highly accurate product demand predictions at scale in the cloud
Product Overview
The Product Demand Forecasting Solution is a cloud-native predictive analytics ML model that analyzes multiple data points, including historical sales data, inventory data, and growth projections to generate up to 50% more accurate product demand forecasts. The solution is scalable and customizable, allows for manual adjustments. It supports batch (schedule) and real-time forecasting, and it can be integrated through RESTful API. The ML model can be used by small- and midsize retailers to cut overstock/stockouts, optimize inventory, and increase supply chain efficiency
Key Data
Version
Type
Algorithm
Highlights
Get the most of your organization’s sales and inventory data to accurately predict demand for your products, thereby reducing overstock and stockouts, cutting waste, and increasing profits
Take advantage of more accurate forecasts to provide the products your customers want when they want them, thereby optimizing product holding costs in warehouses and increasing supply chain efficiency
Need a custom-made solution for product demand forecasting? Reach us at support@vitechlab.com
Not quite sure what you’re looking for? AWS Marketplace can help you find the right solution for your use case. Contact us
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$3/hr
running on ml.c4.xlarge
Model Realtime Inference$3.00/hr
running on ml.c4.xlarge
Model Batch Transform$25.00/hr
running on ml.c4.xlarge
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$0.239/host/hr
running on ml.c4.xlarge
SageMaker Realtime Inference$0.239/host/hr
running on ml.c4.xlarge
SageMaker Batch Transform$0.239/host/hr
running on ml.c4.xlarge
About Free trial
Try this product for 30 days. There will be no software charges, but AWS infrastructure charges still apply. Free Trials will automatically convert to a paid subscription upon expiration.
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 | $3.00 | |
ml.m5.4xlarge | $3.00 | |
ml.m4.16xlarge | $3.00 | |
ml.m5.2xlarge | $3.00 | |
ml.p3.16xlarge | $3.00 | |
ml.m4.2xlarge | $3.00 | |
ml.c5.2xlarge | $3.00 | |
ml.p3.2xlarge | $3.00 | |
ml.c4.2xlarge | $3.00 | |
ml.m4.10xlarge | $3.00 | |
ml.c4.xlarge Vendor Recommended | $3.00 | |
ml.m5.24xlarge | $3.00 | |
ml.c5.xlarge | $3.00 | |
ml.p2.xlarge | $3.00 | |
ml.m5.12xlarge | $3.00 | |
ml.p2.16xlarge | $3.00 | |
ml.c4.4xlarge | $3.00 | |
ml.m5.xlarge | $3.00 | |
ml.c5.9xlarge | $3.00 | |
ml.m4.xlarge | $3.00 | |
ml.c5.4xlarge | $3.00 | |
ml.p3.8xlarge | $3.00 | |
ml.m5.large | $3.00 | |
ml.c4.8xlarge | $3.00 | |
ml.p2.8xlarge | $3.00 | |
ml.c5.18xlarge | $3.00 |
Usage Information
Fulfillment Methods
Amazon SageMaker
Example notebooks for deployment, Real Time inference and Batch Transformation
You can find all the information related to the usage of our product here: https://github.com/VITechLab/aws-sagemaker-examples/tree/master/Forecaster
This repository contains example Jupyter Notebooks showing how to train and deploy the model, run Real Time inference, run Batch Transform job to perform the inference on the data stored in Amazon S3 bucket.
Supported request content type for training is “text/csv” Supported request content type for prediction is “application/json” Supported response type for prediction is “application/json”
You can find more details here: https://github.com/VITechLab/aws-sagemaker-examples/tree/master/Forecaster
Channel specification
Fields marked with * are required
training
Input modes: File
Content types: text/csv
Compression types: None
Additional Resources
End User License Agreement
By subscribing to this product you agree to terms and conditions outlined in the product End user License Agreement (EULA)
Support Information
Retail Store Product Demand Forecasting
If you have any issues or feature requests, please write to us, and we will be happy to help you as soon as possible.
We can also create custom software and models optimised for your specific use case.
Reach us at: support@vitechlab.com
AWS Infrastructure
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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