Sold by: Mphasis
Deployed on AWS
A deep learning based solution to predict the next step and time to next step in a process.
Overview
A deep learning based solution that analyzes event (e.g. loan approval process) log data with contextual information (e.g. loan request parameters, etc.) and predicts the next step and time to next step for an open request within a process. With process execution data stored in form of event logs, an AI based operations planning system can help in understanding future system state based on current state and business context. This solution improves business operations planning by reducing cost and improving efficiency through dynamic resource planning.
Highlights
- The solution takes operational log data as input and provides the answers of these questions: o What is the next possible step/request of a given sequence? o What is the approximate time to the next step? The solution provides the mechanism to train as well as test on user data. This allows for the flexibility to build and predict on user specific process data. The solution is divided into two parts: 1. Process specific training API to capture process behavior 2. Prediction API to predict the next step and time to next step
- From a process manager perspective, next best action prediction can be highly useful for resource planning which can help achieve better throughput rate and time at a lower cost. The solution can be applied to various industries like banking, logistics, insurance etc. and processes such as loan approval process, order fulfillment process, procurement process etc.
- Mphasis Optimize.AI is an AI-centric process analysis and optimization tool that uses AI/ML techniques to mine the event logs to deliver business insights. 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 | $16.00 |
ml.m5.large Inference (Real-Time) Recommended | Model inference on the ml.m5.large instance type, real-time mode | $8.00 |
ml.m5.large Training Recommended | Algorithm training on the ml.m5.large instance type | $10.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $16.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $16.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $16.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $16.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $16.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $16.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $16.00 |
Dimensions summary
You pay by the hour for the compute instances you run, with no upfront commitment. Charges fall into three activity types: training the model, batch inference, and real-time inference. Within each activity, you choose an AWS machine learning instance. Options span general-purpose (m4, m5), compute-optimized (c4, c5), and GPU-accelerated (p2, p3) families in sizes from large to 24xlarge. Larger or GPU-backed instances carry higher hourly rates. Your total cost depends on which instance you pick, the activity type, and the hours it runs.
Top-of-mind questions for buyers
One HostHrs unit is one hour of running the chosen machine learning instance. The software meter counts running time on that instance. If you run two instances at once, each accrues its own hourly charge. Fractional hours are billed based on actual run time.
Each activity bills independently by the hour for the instance you run. Training charges apply while the model learns from data. Batch inference runs predictions on grouped data. Real-time inference serves live predictions. You pay only for the activities you use, and the same instance can carry different rates per activity.
Charges apply per hour while an instance runs. A stopped or terminated instance stops accruing software charges. Real-time inference instances stay active until you shut them down, so they keep billing. Batch and training instances typically run only for the job and stop afterward.
www.mphasis.com
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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
Updated with new features
Additional details
Inputs
- Summary
Input
** Following are the mandatory inputs for both the APIs:**
- CaseID: Unique identifier of a request/journey e.g. E-comm order ID, loan ID etc.
- ActivityID: Activity Identifier/Activity Name performed for each CASE_ID e.g. INVOICE GENERATION, KYC etc.
- CompleteTimestamp: Timestamp for a unique CASE_ID/ACTIVITY_ID combination.
- context: Contextual variables can be anything which provides information related to case. E.g. Loan Amount, Vendor ID etc.
- Limitations for input type
- * Two separate csv input files are required for training and testing * Test dataset should only contain subset of Activity IDs included in the training dataset * Maximum sequence length can not be more than 30
- Input MIME type
- text/csv, text/plain
https://github.com/Mphasis-ML-Marketplace/Optimize.AI-Next-Best-Action-Prediction
https://github.com/Mphasis-ML-Marketplace/Optimize.AI-Next-Best-Action-Prediction
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
CaseID | Unique identifier of a request/journey e.g. E-comm order ID, loan ID etc. | Type: Integer | Yes |
ActivityID | Activity Identifier/Activity Name performed for each CASE_ID e.g. INVOICE GENERATION, KYC etc. | Type: Categorical
Allowed values: INVOICE GENERATION, KYC etc. | Yes |
CompleteTimestamp | Timestamp for a unique CASE_ID/ACTIVITY_ID combination. | Type: FreeText | Yes |
context | Contextual variables can be anything which provides information related to case. E.g. Loan Amount, Insurance Policy Type, Vendor ID etc. | Type: Categorical
Allowed values: Loan Amount, Insurance Policy Type, Vendor ID etc. | Yes |
Resources
Vendor resources
Support
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