Best AWS Marketplace Software - Page 107

Mphasis Optimize.AI MTTR Predictor

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.

Mphasis Scanned Document Tilt Correction

Scanned documents sometimes can have pages with wrong alignment. This can create challenges while processing of documents viz. OCR, ICR, Text extraction, image-based ML/AI modelling, etc. This solution incorporates statistical models which identify angle of tilt based on textual orientation, position of text relative to page boundaries and text clusters and corrects the alignment / tilt of the pages. This enables OCR / ICR engines to achieve higher accuracy and improves the subsequent text extraction pipelines.

Average Rating: 4.0/5.0

Total Reviews: 1

MS Disease State Predictor

The MS Disease State Predictor is an advanced machine learning model designed to assist healthcare professionals in predicting the progression of multiple sclerosis in patients. By analyzing a comprehensive set of clinical and imaging data, this tool provides early and accurate assessments of disease severity, enabling timely therapeutic interventions to potentially slow or prevent further neurological deterioration. Key Features and Functionality: - Multimodal Data Analysis: Integrates various data types, including electronic health records , neuroimaging results, and clinical notes, to offer a holistic view of a patient's condition. - Machine Learning Algorithms: Employs sophisticated algorithms capable of handling complex datasets, ensuring precise predictions of disease activity and progression. - Predictive Modeling: Utilizes models like XGBoost and random forest to forecast disease activity up to six months in advance, with balanced accuracy rates around 80%. - Explainable AI: Incorporates explainable artificial intelligence techniques, such as SHAP , to provide transparent insights into the factors influencing predictions, enhancing trust and interpretability for medical professionals. Primary Value and Problem Solved: The MS Disease State Predictor addresses the critical need for early and accurate identification of disease progression in multiple sclerosis patients. By leveraging advanced machine learning and comprehensive data analysis, it empowers clinicians to make informed decisions regarding treatment plans, potentially improving patient outcomes and quality of life. This predictive capability is essential for implementing timely interventions that can mitigate the impact of the disease.

M-Space: The Maxeler Collaboration Workspace

M-Space: The Maxeler Collaboration Workspace is a secure, high-performance computing platform designed to facilitate collaborative research and development. It integrates Maxeler's advanced dataflow computing technology with fragmentiX's multi-cloud storage solutions, ensuring both computational efficiency and robust data protection. Key Features and Functionality: - High-Performance Computing: Utilizes Maxeler's dataflow engines to deliver maximum performance density for complex data processing tasks. - Secure Data Storage: Incorporates fragmentiX's storage architecture, which employs Shamir's Secret Sharing to distribute data fragments across multiple cloud vendors, enhancing data secrecy and resilience. - Scalability: Supports seamless scaling across various public cloud platforms, as well as hybrid and on-premise storage configurations, to meet diverse computational and storage needs. Primary Value and User Solutions: M-Space addresses the critical need for a collaborative environment that combines high computational performance with stringent data security measures. By leveraging FPGA-based processing and advanced data fragmentation techniques, it offers researchers and developers a platform where sensitive data can be processed and stored securely, without compromising on performance. This is particularly beneficial for sectors handling confidential information, such as medical and genomic research, ensuring digital sovereignty and long-term data privacy.

Multi-Port Forward Server

The Multi-Port Forward Server, or port address translation server (PAT), is a public facing virtual appliance which accepts inbound UDP and TCP traffic on user-defined ports. The traffic is then forwarded in real-time to either an internal private IP address or an external IP address (NAT), specifically to a user-defined destination port. Users have the ability to specify multiple listening ports and forward those connections to multiple destinations.

MultiSequence Benchmark

MultiSequence Benchmark is a hosted solution designed to measure the performance of websites, web applications, or APIs using the HTTP protocol. It offers pre-built benchmarking scripts and supports the upload of custom Apache JMeter scripts, enabling users to simulate load on applications or endpoints to assess their robustness and analyze performance under varying conditions. The platform features a smartphone-friendly user interface, allowing users to re-run saved benchmarks, track the history of previous tests, and download comprehensive benchmark reports. Key Features and Functionality: - Performance Measurement: Evaluate the performance of websites, applications, or APIs using HTTP protocols. - Custom Script Support: Upload and utilize custom Apache JMeter scripts for tailored load simulations. - Benchmark Management: Re-run saved benchmarks and maintain a history of previous tests for comparative analysis. - User-Friendly Interface: Access a smartphone-friendly interface for convenient benchmarking on the go. - Report Generation: Download detailed benchmark reports to document and share performance insights. Primary Value and User Solutions: MultiSequence Benchmark addresses the critical need for organizations to ensure the reliability and efficiency of their web services. By providing tools to simulate various load conditions and analyze performance metrics, it empowers users to identify potential bottlenecks, optimize resource allocation, and enhance the overall user experience. The ability to upload custom scripts and re-run benchmarks facilitates continuous performance monitoring and improvement, ensuring that applications remain robust and responsive under different traffic scenarios.

Mumble Server - VoIP solution powered by TurnKey GNU/Linux

Mumble Server, powered by TurnKey GNU/Linux, is an open-source, low-latency, high-quality voice chat solution designed primarily for gamers and other communities requiring reliable voice communication. This ready-to-run appliance simplifies the deployment of Mumble's server component, Murmur, enabling users to establish secure and efficient voice chat servers with minimal setup. Built on Debian GNU/Linux, it ensures stability and security, making it an ideal choice for both personal and professional use. Key Features and Functionality: - Low-Latency Communication: Provides real-time voice chat with minimal delay, enhancing the user experience during interactive sessions. - High-Quality Audio: Utilizes the Opus codec to deliver clear and crisp sound quality. - Secure Encryption: Ensures all communications are encrypted, maintaining user privacy and data security. - Hierarchical Channel Structure: Supports a root channel with a hierarchical tree of sub-channels, allowing organized and scalable communication setups. - Access Control Lists (ACLs: Offers extensive user permission configurations through ACLs, enabling precise control over user access and actions. - Positional Audio Support: For compatible games, provides positional audio, giving a sense of direction and distance to voices based on in-game positions. - Cross-Platform Compatibility: The client is available on multiple operating systems, including Windows, Linux, macOS, Android, and iOS. Primary Value and User Solutions: Mumble Server addresses the need for a reliable, secure, and high-performance voice communication platform, especially for gaming communities and collaborative teams. Its low-latency and high-quality audio ensure seamless communication, while the robust security features protect user data. The hierarchical channel structure and ACLs provide flexibility in managing user permissions and organizing discussions. Additionally, the inclusion of positional audio enhances the immersive experience in compatible games. By offering an easy-to-deploy solution with TurnKey GNU/Linux, users can quickly set up and maintain their own voice chat servers without extensive technical expertise.

MXNet 1 Python 2.7 CPU Production

The "MXNet 1 Python 2.7 CPU Production" is an Amazon Machine Image designed to facilitate the development and deployment of deep learning models using Apache MXNet on Amazon EC2 instances. This AMI is pre-configured with MXNet version 1.x and Python 2.7, optimized for CPU-based computations, providing a ready-to-use environment for machine learning practitioners. Key Features and Functionality: - Pre-Installed MXNet Framework: The AMI comes with Apache MXNet 1.x, an open-source deep learning framework known for its scalability and efficiency in training deep neural networks. - Python 2.7 Environment: It includes Python 2.7, allowing users to develop and run their deep learning applications seamlessly. - CPU Optimization: Tailored for CPU-based instances, this AMI ensures efficient utilization of computational resources without the need for GPU acceleration. - Ready-to-Use Setup: The AMI is configured with all necessary dependencies, enabling users to start developing and deploying models immediately without additional setup. Primary Value and Problem Solved: This AMI addresses the challenge of setting up a deep learning environment by providing a pre-configured, CPU-optimized platform for developing and deploying MXNet-based models. It eliminates the complexities associated with manual installation and configuration, allowing users to focus on building and training their models efficiently. Note: As of September 2023, Apache MXNet has reached its end of life and is no longer actively developed. Users are advised to consider this when planning their deep learning projects.

MXNet 1 Python 3.6 CPU Production

The MXNet 1 Python 3.6 CPU Production environment is a pre-configured software stack that integrates Apache MXNet, an open-source deep learning framework, with Python 3.6. This setup offers a stable and tested execution environment optimized for CPU-based tasks, facilitating efficient training, inference, and deployment of deep learning models. It is designed to seamlessly integrate into continuous integration and deployment workflows, making it suitable for both short and long-running high-performance tasks. Key Features and Functionality: - Pre-configured Environment: Combines Apache MXNet with Python 3.6, providing a ready-to-use platform for deep learning applications. - CPU Optimization: Tailored for CPU-based computations, ensuring efficient performance without the need for GPU resources. - Integration Capabilities: Easily integrates into existing continuous integration and deployment pipelines, streamlining the development and deployment process. - Versatility: Suitable for a range of tasks, from training and inference to serving as an API service, accommodating various deep learning workflows. Primary Value and User Solutions: This environment addresses the need for a reliable and efficient platform for developing and deploying deep learning models without the complexities of manual setup. By offering a pre-configured stack optimized for CPU usage, it enables users to focus on model development and deployment, reducing setup time and potential configuration errors. Its integration-friendly design ensures that it can be seamlessly incorporated into existing workflows, enhancing productivity and facilitating the rapid deployment of machine learning solutions.

MXNet 1 Python 3.6 NVidia GPU Production

MXNet, an open-source deep learning framework, and Python, a high-level programming language for general-purpose programming

Myriad360 Cloud Security Best Practice Review (CS-BPR)

Myriad360 will provide the review and assessment of the existing security controls including the review of the native AWS security policies.

Myriad360 Cloud Security Design & Deployment

Myriad360 will provide firewall design and deployment services around security including review of the native AWS security policies. The services include secured connectivity to AWS resources as well.

MyRocks 5.6

MyRocks 5.6 is an open-source storage engine for MySQL, developed by Facebook, that integrates RocksDB into MySQL 5.6. It is designed to enhance database performance by improving space efficiency, write efficiency, and read efficiency, particularly on flash storage. By utilizing a Log-structured Merge-Tree (LSM-Tree architecture, MyRocks offers significant advantages over traditional storage engines like InnoDB. Key Features and Functionality: - Greater Space Efficiency: MyRocks provides up to 2x better compression compared to compressed InnoDB and 3-4x better compression compared to uncompressed InnoDB, resulting in reduced storage requirements. - Greater Writing Efficiency: With 10x less write amplification than InnoDB, MyRocks enhances the endurance of flash storage and improves overall throughput. - Faster Replication: MyRocks eliminates random reads for updating secondary keys (except for unique indexes and offers a Read-Free Replication option that removes random reads when updating primary keys, regardless of uniqueness, using a row-based binary logging format. - Faster Data Loading: By writing data directly to the bottommost level, MyRocks avoids compaction overheads, enabling faster data loading when this feature is enabled for a session. Primary Value and Problem Solved: MyRocks 5.6 addresses the challenges of managing large-scale, write-intensive workloads by offering a storage engine that significantly reduces storage space requirements and write amplification. This leads to improved performance and cost savings, especially for applications running on flash storage. By enhancing write efficiency and providing better compression, MyRocks enables organizations to handle larger datasets more effectively, ensuring faster replication and data loading processes.

MySQL 5.7.33 Pre-configured with support by Bansir

MySQL Enterprise Scalability enables you to meet the sustained performance and scalability requirements of ever increasing user,Stable and production ready, with long-term support and updates

MySQL 5.7 With RHEL 7

MySQL 5.7.33 is one of the most popular open source relational databases. It's made by the original developers of MySQL and guaranteed to stay open source.