Products

CacheSight enables developers and dev-ops engineers to measure performance and troubleshoot issues for ElastiCache for Memcached. CacheSight helps to determine if your caching strategy is working as planned by delivering unique insight into the performance of your cache.

This solution analyses a corpus of text to predict whether a person is a potential lead for education loan.

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 rig

Absenteeism at work forecasting generates 30 days of forward forecast of employee absenteeism using historical data. This solution helps businesses to optimize their workforce and related infrastructure in an efficient manner. 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.

Customer churn refers to the loss of existing clients or customers. This solution identifies mobile network subscribers who are more likely to change their operator. During the training stage, the solution automatically conducts feature interaction on the training data and selects a subset of features based on feature importance. It then trains multiple models and identifies the best performing model. This model is then selected for prediction on new 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.

With paGo Commerce, users are able to sell items on any device, anywhere. Other key features include a built-in shipping table, currency converter, social integration, and coupons.

Geographical Entity Sentiment Analysis identifies positive, negative or neutral sentiments related to geographical entities such as cities, states, countries etc. Polarity scores are calculated by identifying named entities in text and modeling sentiments to respective entities. This solution can be used to identify sentiments around specific locality or a comparative study between two locations based on different features like property rates, local facilities, proximity with prominent localitie

Apache Cassandra is an open-source, distributed, wide-column store, NoSQL database management system designed to handle large amounts of data across many commodity servers, providing high availability with no single point of failure. Cassandra offers robust support for clusters spanning multiple datacenters, with asynchronous masterless replication allowing low latency operations for all clients. This AMI could be used for setting up a single node Cassandra installation or used as a node for set

Apache Cassandra is an open-source, distributed, wide-column store, NoSQL database management system designed to handle large amounts of data across many commodity servers, providing high availability with no single point of failure. Cassandra offers robust support for clusters spanning multiple datacenters, with asynchronous masterless replication allowing low latency operations for all clients. This AMI could be used for setting up a single node Cassandra installation or used as a node for set

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 Agr

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

The solution analyzes reviews of mobile phones and accessories and classifies them into positive and negative sentiments. It uses text analysis, natural language processing, machine learning techniques to predict the sentiment classes. It can be used to analyze product feedback from customers by predicting the sentiments of reviews.

Energy Consumption Forecasting generates 30 months of forward forecast of the consumption 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.

A high frequency of issues can generate an overwhelming number of help desk tickets and incorrect delegation to teams to handle them. This leads to a spike in MTTR (mean time taken to resolve) and a dip in FCR (First Call Resolution). The solution mitigates these issues by training a multi-factor ML model that considers factors like ticket impact, urgency, priority, issue description and other features to predict the most relevant group to resolve a ticket. A pool of models is run through data t

PACE ML Text Augmentation helps in preparing datasets by creating a more comprehensive set of possible data points and their distribution. The module provides a solution to the limited data problem for Natural Language Processing techniques. A combination of transformations and generations are used to increase the size and quality of the training data.

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 th

Categorical Missing Data Imputation is a robust deep learning based solution. This solution fills in missing values for categorical attributes by identifying data patterns in the input dataset. It helps reduce the data quality issues due to incomplete / non-available data.

ZendPHP with Apache on CentOS 8 for EC2 provides a turnkey solution for running PHP in the cloud. It's a supported PHP runtime that can scale seamlessly across cloud resources, from the company that helped write PHP.

Customer churn refers to the loss of existing clients or customers. This solution identifies bank customers who are more likely to close their account and leave the bank. During the training stage, the solution automatically conducts feature interaction on the training data and selects a subset of features based on feature importance. It then trains multiple models and identifies the best performing model. This model is then selected for prediction on new data.