Sold by: MphasisÂ
Deployed on AWS
This is an AutoML solution which runs multiple ML models on the user data and selects the best model.
Overview
This AutoML solution runs several classification and regression machine learning models on the input data. It will identify the best performing model based on the user specified evaluation metric. This will simplify the task of model building for a data scientist where the user will have to specify few selected parameters to find the best model for the data set.
Highlights
- This solution will help identify the best machine learning model for the data set given the evaluation metric.
- This solution saves a significant amount of time spent over developing and running different preprocessing operations on the user data.
- PACE - ML is Mphasis Framework and Methodology for end-to-end machine learning development and deployment. PACE-ML enables organizations to improve the quality & reliability of the machine learning solutions in production and helps automate, scale, and monitor them. Need customized Machine Learning and Deep Learning solutions? Get in touch!
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.m5.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $20.00 |
ml.t2.medium Inference (Real-Time) Recommended | Model inference on the ml.t2.medium instance type, real-time mode | $10.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $20.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $20.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $20.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $20.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $20.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $20.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $20.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $20.00 |
Vendor refund policy
Currently we do not support refunds, but you can cancel your subscription to the service at any time.
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)Â .
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 model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a 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
This is the sixth version of the algorithm with few bug fixes.
Additional details
Inputs
- Summary
This algorithm takes a zip file as an input. This zip file should contain exactly two files:
- Data.csv – This will be the data on which algorithm will run its tasks.
- Config.json – This file should contain parameters specific to algorithm to execute tasks on the supplied data. The available parameter are as follows with their available values:
- Input MIME type
- application/zip
https://github.com/Mphasis-ML-Marketplace/paceml-automl/tree/main/input
https://github.com/Mphasis-ML-Marketplace/paceml-automl/tree/main/input
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
Target_variable | Specify the target variable from the input dataset. | Type: FreeText | Yes |
Is_classification | Specify whether problem is classification or regression. | Type: Categorical
Allowed values: True,False | Yes |
Blacklist | Specify models which are to be ignored | Type: FreeText | Yes |
Folds | Specify the number of validation steps to run. | Type: Integer | Yes |
Turbo | Specify whether to run ensemble models. | Type: Categorical
Allowed values: True, False | Yes |
Optimize_on | Depending on the problem specify the metric to rank the models on. | Type: FreeText | Yes |
Resources
Vendor resources
Support
Vendor support
For any assistance reach out to us at:
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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