Sold by: RocketML
Deployed on AWS
Free Trial
Regression on LibSVM data type using Gradient Boosting Decision Trees
Overview
A Gradient Boosted Decision Tree for regression 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
- Leaf-wise tree growth algorithm to achieve lower loss
Details
Sold by
Categories
Delivery method
Latest version
Deployed on AWS
New
Introducing multi-product solutions
You can now purchase comprehensive solutions tailored to use cases and industries.
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
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.c5.xlarge Inference (Real-Time) | Model inference on the ml.c5.xlarge instance type, real-time mode | $0.20 |
ml.c5.4xlarge Inference (Real-Time) | Model inference on the ml.c5.4xlarge instance type, real-time mode | $0.20 |
ml.c5.9xlarge Inference (Real-Time) | Model inference on the ml.c5.9xlarge instance type, real-time mode | $0.20 |
Dimensions summary
You pay by the hour based on three activity types: training, batch inference, and real-time inference. Within each activity, you pick one of five ml.c5 instance sizes, from xlarge up to 18xlarge. Larger instances carry more compute capacity. Training runs this gradient boosting regression algorithm on your data. Batch inference scores data in bulk, while real-time inference serves live predictions. All 15 options bill per host hour, so your cost scales with how long each instance runs and which size you choose.
Top-of-mind questions for buyers
Each size reflects a different amount of CPU compute capacity on a single AWS EC2 instance. The algorithm scales across multiple cores within one instance. Larger sizes like 18xlarge provide more cores, which can shorten training or inference time for bigger or more complex datasets.
Charges apply per host hour while an instance runs. When you stop an instance, the hourly software charge stops. You pay only for the time each training, batch inference, or real-time inference instance is active. Underlying AWS infrastructure fees are separate from this software charge.
Training, batch inference, and real-time inference bill independently. You pay per host hour for each activity you run, at the rate for the instance size you pick. Training builds the model; batch inference scores data in bulk; real-time inference serves live predictions. Each appears separately on the same invoice.
www.rocketml.net
Helpful?
Vendor refund policy
We do not currently support refunds, but you can cancel at any time. Please contact us at info@rocketml.net
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA) .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
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
Gradient Boosting Decision Trees for Regression on Sparse Dataset
Additional details
Inputs
- Summary
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.rocketml.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.
Similar products
Classification on LibSVM data type using Gradient Boosting Decision Trees
Regression on LibSVM data type using Random Forests
Binary classification for LibSVM data type
Classification on dense data using Gradient Boosting Decision Trees
Customer reviews
No customer reviews yet
Be the first to review this product . We've partnered with PeerSpot to gather customer feedback. You can share your experience by writing or recording a review, or scheduling a call with a PeerSpot analyst.