Sold by: Outpace SystemsÂ
Overview
An implicit feedback matrix factorization model. Uses a classic matrix factorization approach, with latent vectors used to represent both users and items. Their dot product gives the predicted score for a user-item pair. The model is trained through negative sampling: for any known user-item pair, one or more items are randomly sampled to act as negatives (expressing a lack of preference by the user for the sampled item).
Highlights
- Please note: Models trained on GPU instances must use GPU instances for inference. Same applies to CPU-trained models.
- Feature roadmap: * allow exclude_items during inference (to exclude items already purchased/viewed by the user) * support pipe mode * support for supplying interaction weighting values
- Now supports parameter tuning. Requires training and testing data channel for tuning.
Details
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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.p3.2xlarge Inference (Batch) Recommended | Model inference on the ml.p3.2xlarge instance type, batch mode | $0.00 |
ml.p3.2xlarge Inference (Real-Time) Recommended | Model inference on the ml.p3.2xlarge instance type, real-time mode | $0.00 |
ml.p3.2xlarge Training Recommended | Algorithm training on the ml.p3.2xlarge instance type | $0.00 |
ml.m5.large Inference (Batch) | Model inference on the ml.m5.large instance type, batch mode | $0.00 |
ml.m5.xlarge Inference (Batch) | Model inference on the ml.m5.xlarge instance type, batch mode | $0.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $0.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $0.00 |
ml.m5.12xlarge Inference (Batch) | Model inference on the ml.m5.12xlarge instance type, batch mode | $0.00 |
ml.m5.24xlarge Inference (Batch) | Model inference on the ml.m5.24xlarge instance type, batch mode | $0.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $0.00 |
Vendor refund policy
No refunds.
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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
Adds support for hyperparameter tuning. Tuning requires testing data channel in addition to traning.
Additional details
Inputs
- Summary
See example notebook for example usage.
- Input MIME type
- application/json
See Input Summary
See Input Summary
Resources
Vendor resources
Support
Vendor support
recsys@outpace.com see attached example notebook
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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