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.
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Mphasis HyperGraf Market Basket Analysis
By:
Latest Version:
3.5
The solution identifies items bought together frequently in a retail shopping cart.
Product Overview
Market Basket Analysis uncovers associations between articles and identifies the frequent products which are likely to be purchased together by analyzing large volumes of transactional data. Knowing what products people purchase together can be advantageous to an e-commerce website or any retailer store in preparing recommendations and promotions.
Key Data
Version
By
Categories
Type
Model Package
Highlights
Identifies the products purchased together frequently and generates association rules with details of antecedent support, rule support, confidence, lift, conviction and leverage.
Anticipate customer purchase behaviour by using statistical affinity calculations to increase cross-selling and make promotions more effective.
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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
Model Realtime Inference$4.00/hr
running on ml.m5.large
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 Realtime Inference$0.115/host/hr
running on ml.m5.large
SageMaker Batch Transform$0.115/host/hr
running on ml.m5.large
Model Realtime Inference
For model deployment as Real-time endpoint 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 | Realtime Inference/hr | |
---|---|---|
ml.m4.4xlarge | $4.00 | |
ml.g4dn.4xlarge | $4.00 | |
ml.m5.4xlarge | $4.00 | |
ml.m4.16xlarge | $4.00 | |
ml.m5.2xlarge | $4.00 | |
ml.p3.16xlarge | $4.00 | |
ml.r5.large | $4.00 | |
ml.g4dn.2xlarge | $4.00 | |
ml.m4.2xlarge | $4.00 | |
ml.r5.12xlarge | $4.00 | |
ml.c5.2xlarge | $4.00 | |
ml.p3.2xlarge | $4.00 | |
ml.r5.xlarge | $4.00 | |
ml.c4.2xlarge | $4.00 | |
ml.g4dn.12xlarge | $4.00 | |
ml.m4.10xlarge | $4.00 | |
ml.c4.xlarge | $4.00 | |
ml.m5.24xlarge | $4.00 | |
ml.c5.xlarge | $4.00 | |
ml.g4dn.xlarge | $4.00 | |
ml.r5.24xlarge | $4.00 | |
ml.p2.xlarge | $4.00 | |
ml.m5.12xlarge | $4.00 | |
ml.g4dn.16xlarge | $4.00 | |
ml.p2.16xlarge | $4.00 | |
ml.c4.4xlarge | $4.00 | |
ml.r5.4xlarge | $4.00 | |
ml.c5.large | $4.00 | |
ml.m5.xlarge | $4.00 | |
ml.c5.9xlarge | $4.00 | |
ml.m4.xlarge | $4.00 | |
ml.c5.4xlarge | $4.00 | |
ml.p3.8xlarge | $4.00 | |
ml.c4.large | $4.00 | |
ml.m5.large Vendor Recommended | $4.00 | |
ml.c4.8xlarge | $4.00 | |
ml.p2.8xlarge | $4.00 | |
ml.g4dn.8xlarge | $4.00 | |
ml.t2.xlarge | $4.00 | |
ml.c5.18xlarge | $4.00 | |
ml.t2.large | $4.00 | |
ml.r5.2xlarge | $4.00 | |
ml.t2.medium | $4.00 | |
ml.t2.2xlarge | $4.00 |
Usage Information
Model input and output details
Input
Summary
- Sample input file:
| InvoiceID | SKUID | Item | CUSTOMERID | |-----------|--------|------------|------------| | 100101 | 989898 | APPLE 1 KG | 501011 |
- Input file should have following columns:
- InvoiceID: This is the Invoice Number which is the systematically assigned sequential code unique to each invoice.
- SKUID: Stock Keeping Unit ID.
- Item: description of item, a string, name of item along with brand name and color name.
Input MIME type
text/csv , text/plainSample input data
Output
Summary
The output file (in csv format) contains the following columns:
- Item in cart
- Recommendation
Generated results are sorted in the decreasing order of item support and can be filtered based on rule support, confidence and lift.
Output MIME type
text/csv, text/plainSample output data
Sample notebook
Additional Resources
End User License Agreement
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Support Information
Mphasis HyperGraf Market Basket Analysis
For any assistance, please reach out at:
AWS Infrastructure
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Learn MoreRefund Policy
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