Sold by: MphasisÂ
Deployed on AWS
The solution provides 30 days forecast of server utilization using historical usage data.
Overview
Server Utilization Forecasting enables enterprises to optimize server allocation and utilization by generating 30 days of forward forecast of server usage. This helps enterprises to plan their server allocation strategy across the cloud and on premise scenarios using historical data. It uses ensemble ML algorithms with automatic model selection. This solution performs automated model selection to apply the right model based on the input data, thereby providing consistent and better results
Highlights
- This solution will take in daily data as input and provide 30 days future forecast. Automatic model selection will automatically identify the set of optimal algorithms and combine their results using ensemble learning to provide the results.
- Mphasis Time Series Forecasting can be applied in Server Utilization Demand Forecasting
- Need customized Deep Learning and Machine Learning solutions? Get in touch!
Details
Sold by
Categories
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 | $10.00 |
ml.m5.large Inference (Real-Time) Recommended | Model inference on the ml.m5.large instance type, real-time mode | $5.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $10.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $10.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $10.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $10.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $10.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $10.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $10.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $10.00 |
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
Bug Fixes and Performance Improvement
Additional details
Inputs
- Summary
Input
• Supported content types: text/csv • Sample input file: Sample input Â
maskedsku 2015-04-04 F0007 1338 Input should have - Have an unique identifier column called 'maskedsku'. eg. maskedsku can be your shipment id.
- The date format of the columns should be: 'YYYY-MM-DD'
Output
• Content type: text/csv • Sample output file: Sample OutputÂ
maskedsku 2015-04-04 20211101_forecast F0007 1338 1894 Invoking endpoint
AWS CLI Command
If you are using real time inferencing, please create the endpoint first and then use the following command to invoke it:
!aws sagemaker-runtime invoke-endpoint --endpoint-name $model_name --body fileb://$file_name --content-type 'text/csv' --region us-east-2 result.csvSubstitute the following parameters:
- model_name - name of the inference endpoint where the model is deployed
- file_name - input csv name
- text/csv - MIME type of the given input
- result.csv - filename where the inference results are written to.
Resources
- Input MIME type
- text/csv
See Input Summary
See Input Summary
Resources
Vendor resources
Support
Vendor support
For any assistance, please reach out 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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