Sold by: RocketML
Deployed on AWS
Classification on dense data using Random Forests
Overview
Random Forest classification on dense 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 csv, tsv dataset into other formats
- Leaf-wise tree growth algorithm to achieve lower loss
Details
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Delivery method
Latest version
Deployed on AWS
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Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
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.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.c4.2xlarge Inference (Batch) | Model inference on the ml.c4.2xlarge instance type, batch mode | $0.20 |
ml.c4.8xlarge Inference (Batch) | Model inference on the ml.c4.8xlarge 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.c5.18xlarge Inference (Batch) | Model inference on the ml.c5.18xlarge instance type, batch mode | $0.20 |
ml.c4.4xlarge Inference (Batch) | Model inference on the ml.c4.4xlarge instance type, batch mode | $0.20 |
ml.c5.xlarge Inference (Batch) | Model inference on the ml.c5.xlarge instance type, batch mode | $0.20 |
ml.c5.9xlarge Inference (Batch) | Model inference on the ml.c5.9xlarge instance type, batch mode | $0.20 |
Dimensions summary
You pay by the hour for the compute instance you run, with no upfront commitment. Pricing splits into three activities: training a model, running batch inference, and running real-time inference. Each activity is offered across nine AWS instance sizes in the c4 and c5 families. Costs scale with the instance you pick — larger instances carry higher hourly rates. You choose the size based on your data and performance needs. Batch inference processes data in groups, while real-time inference serves predictions on demand. Total cost depends on the instance type, the activity, and the hours used.
Top-of-mind questions for buyers
You pay per host-hour only while the instance runs. A stopped instance accrues no software charges. The meter counts active running time for the activity and instance type you selected. Underlying AWS storage or related resources may still bill separately through your AWS account.
One HostHrs unit is one hour that a single compute instance runs your chosen activity. The activity is training, batch inference, or real-time inference. Each instance type in the c4 and c5 families has its own hourly rate. The clock runs while the instance is active.
Both bill per host-hour on the same c4 and c5 instance types. Batch inference runs only while processing a group of data, so you pay for that run window. Real-time inference keeps an endpoint running to serve predictions on demand, so you pay for all hours the endpoint stays up.
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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
Random Forest Algorithm for classification on dense datasets
Additional details
Inputs
- Summary
See example notebook for example usage.
See Input Summary
See Input Summary
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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