Mphasis
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Scanned documents can have pages with wrong alignment, low contrast, low brightness and rotated upside down. This can create challenges while processing of documents using OCR, ICR, Text extraction, image-based ML/AI modelling, etc. This solution incorporates statistical models which identify angle of tilt based on textual orientation and position of text relative to page boundaries and corrects the alignment / tilt of the pages. It identifies the contrast between background and text in the pages of the scanned document and adjust the contrast of the low contrast pages. It also incorporates deep learning models which identify if a page is upside down. The models are trained on a large dataset of thousands of pages. This enables OCR/ICR engines to achieve higher accuracy and improves the subsequent text extraction pipelines.
The MTTR (Mean Time to Resolution) predictor is an AI/ML based solution which predicts the time taken by a service agent to solve a specific ticket or an incident request. The solution learns the efficiency, experience and workload management metrics for various ticket types solved by service agents to arrive at the predictions. The solution helps business in optimal ticket allocation leading to a low MTTR, shorter wait time, fewer open incidents leading to improved efficiency and SLA (Service Level Agreement) adherence.
Cloud storage cost forecasting helps businesses assess the cost incurred from their cloud storage based on historic data. This will help businesses get an understanding of the potential cost for their cloud resources and help them plan better to manage storage services like S3 buckets, EC2 storage, Elastic Block Store, Amazon Glacier etc. it uses ensemble ML algorithms with automatic model selection algorithms. This solution performs automated model selection to apply the right model based on the input data.
Missing Data Imputation is a robust neural network based solution. This solution fills in missing values for numerical attributes by identifying data patterns in the input dataset. It helps reduce the data quality issues due to incomplete/non-available data.
Data evolves over time, causing a change in the distributions and interpretation of data and a corresponding degradation in model performance. The Drift Detector uses an incremental learning method, in which each incoming instance retrains the model. The solution detects drifts in the model output, providing useful insights with respect to the data and model behavior. This helps businesses identify degradation in model performance and need for retraining.
Expert Identifier is machine learning based model that uses information present in any incident/ticket management data such as: Ticket ID, Ticket Solver Id, Ticket Priority, Ticket Category, Ticket Submission and Resolved date and identifies the right expert to be assigned to a specific ticket or incident request. It can optimise ticket allocation, decreases the ticket resolution time and improve KPIs (Key Performance Indicators) such as customer satisfaction, adherence to SLA (Service Level Agreement), MTTR (Mean Time to Resolve), cost to company, etc.
Inventory Forecasting generates 30 months of forward forecast of the Inventory using historical data. It uses ensemble ML algorithms with automatic model selection algorithms. This solution provides consistent and better results due to its ensemble learning approach. This solution performs automated model selection to apply the right model based on the input data.
InfraGraf Network Traffic Forecasting helps businesses get a future forecast of the network traffic based on historic data. Benefits offered by this solution includes accurate forecast of network traffic which enables better planning for network infrastructure, application scalability and auto scaling. It uses ensemble ML algorithms with automatic model selection algorithms. This solution provides consistent and better results due to its ensemble learning approach. This solution performs automated model selection to apply the right model based on the input data.
Mphasis time series ticket forecasting helps businesses predict the number of tickets of a specific type based on historic data. This will help businesses assess the level of automation as well as human intervention required to resolve the issues and plan accordingly. It uses ensemble ML algorithms with automatic model selection algorithms. This solution provides consistent and better results due to its ensemble learning approach. This solution performs automated model selection to apply the right model based on the input data.
Mphasis Reviews
Ravi B.
Looking for Big data and data engineering projects having spark,hadoop,hive,talend,sql,java implementationsPerfect text correction tool
Verified User in Marketing and Advertising
Simple, Fast, and Reliable Paraphrasing Tool
Tiwari S.
Sr Infrastructure Engineer AWS Certified Solution Architect AssociateHyperGraf Home Loan Lead Review
About
What is Mphasis?
Mphasis Stelligent, with its website located at https://stelligent.com/, specializes in providing DevOps automation and continuous delivery solutions on the Amazon Web Services (AWS) cloud platform. As part of Mphasis, a larger IT services company, Stelligent focuses on helping clients automate and accelerate the development, testing, and deployment of applications within AWS environments. Their suite of services includes consulting, engineering, and automation expertise to implement secure and scalable CI/CD pipelines, facilitating a faster go-to-market strategy for enterprises across various sectors. Stelligent's approach integrates tightly with AWS technologies, offering tools and practices that enhance the cloud capabilities of their customers, ensuring efficient and innovative cloud-based solutions.