Sold by: RocketML
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Binary classification for LibSVM data type
Overview
A logistic regression model for binary classification on sparse data set like LibSVM without translating the data set into other formats like recordIO. The algorithm scales efficiently across multi-cores on a single AWS EC2 Instance out of the box.
Highlights
- Users don't have to translate their LibSVM data set into other formats
Details
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Delivery method
Latest version
Deployed on AWS
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Pricing
Free trial
Try this product free for 7 days according to the free trial terms set by the vendor.
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.c5.2xlarge Inference (Batch) Recommended | Model inference on the ml.c5.2xlarge instance type, batch mode | $0.20 |
ml.c5.2xlarge Inference (Real-Time) Recommended | Model inference on the ml.c5.2xlarge instance type, real-time mode | $0.20 |
ml.c5.2xlarge Training Recommended | Algorithm training on the ml.c5.2xlarge instance type | $0.20 |
ml.c5.xlarge Inference (Batch) | Model inference on the ml.c5.xlarge instance type, batch mode | $0.20 |
ml.c5.4xlarge Inference (Batch) | Model inference on the ml.c5.4xlarge instance type, batch mode | $0.20 |
ml.c5.9xlarge Inference (Batch) | Model inference on the ml.c5.9xlarge instance type, batch mode | $0.20 |
ml.c5.18xlarge Inference (Batch) | Model inference on the ml.c5.18xlarge instance type, batch mode | $0.20 |
ml.c4.xlarge Inference (Batch) | Model inference on the ml.c4.xlarge instance type, batch mode | $0.20 |
ml.c4.2xlarge Inference (Batch) | Model inference on the ml.c4.2xlarge instance type, batch mode | $0.20 |
ml.c4.4xlarge Inference (Batch) | Model inference on the ml.c4.4xlarge instance type, batch mode | $0.20 |
Dimensions summary
You pay by the host hour based on the AWS instance type you choose. Pricing splits into three activities: training a model, running batch inference, and running real-time inference. Within each activity, you select from a range of compute-optimized instance sizes in the c4 and c5 families. Larger instance sizes carry more compute capacity, so hourly rates rise as you move up in size. You only pay while an instance runs, so costs scale with how long and how often you use each one.
Top-of-mind questions for buyers
Both meter by host hour on the same instance types. Batch mode runs predictions on a dataset as a job, then stops. Real-time mode keeps an endpoint running to serve predictions on demand. Real-time hours accrue while the endpoint stays live, so keeping it up longer raises cost.
You pay by the host hour only while an instance runs. Batch training and batch inference jobs stop when the task finishes, so billing ends then. Real-time endpoints keep accruing host-hour charges until you shut them down. Underlying AWS infrastructure fees may apply separately.
Each family offers compute-optimized sizes from xlarge upward. You pick the family and size that fit your data and model. Larger sizes add compute capacity for faster runs on big workloads. The software scales across multiple cores on a single instance.
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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
Beta version for preview
Additional details
Inputs
- Summary
Usage Instructions: See example notebook for example usage.
See Input Summary
See Input Summary
Support
Vendor support
RocketML offers free support with email response as well as one-on-one office hours with our experienced solution architects. The service helps customers of all sizes and technical abilities to make best use of RocketML products and features. Subscribers and prospecting customers can contact us at the URL below or email support@rocketml.net <www.rocktml.net >
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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