MDACA Edge computing technology is designed to meet the demands for efficiency, lower latency, and more reliability among internet-connected devices and systems. While cloud computing has traditionally served as a reliable and cost-effective means for connecting many of these devices to the internet, the continuous rise of Internet of Things (IoT) and mobile computing has created the demand for faster network availability. Integrating edge computing with cloud data as a service solves this need that includes enhancing the capability of the development teams to refine and expand upon the Machine Learning (ML) and Artificial Intelligence (AI) algorithms required at the edge to maximize the value of the data. Additionally our edge devices are designed to leverage DoD-grade security leveraging smart card assess technology such as common access card (CAC) and personal identity verification (PIV) cards. Our edge technology supports the MDACA platform in a box, is designed to support integrated, secure, scalable and flexible infrastructures that can be deployed worldwide in support of near-time data collections for a wide range of data solutions. As part of the digital transformation with the introduction of IoT devices and the evolving challenges of supporting systems in both connected and disconnected modes, MDACA’s edge node capability in support of data collection geared to provide advancement in the capabilities of autonomous execution.
Digital Twin Support
Support for this autonomous execution goes beyond the standard data collection; the data needs to be collected in a manner to be able to provide data scientists and developers with a 360 degree view of the execution ecosystem. Data scientists and developers need to be able to support the concepts of creating a digital twin of the operating environment which is a virtual reflection of real-world assets and infrastructure. These digital twins must create a trusted, accurate model of the real world to be useful. Our goal in supporting this effort is to play a foundational role in supporting the data collection in supporting the creation of a digital twin ecosystem by bridging the real world to the virtual. We bring the capability of data collection in support of artificial intelligence (AI), machine learning (ML), insights and operations systems which are built on the foundation of trusted remote data. Data from remote real-world sources must be verified with proof of origin, making it trusted at collection, in transit, and at rest; this data is the raw material on which virtual tools are constructed.
Digital Innovation Lab in support of IoT/EDGE Computing
Our existing DoD-certified, supported framework provides a Digital Innovation (DIGIN) Lab, providing managed integrated tools that can be easily deployed in DoD cloud environments, on- premises, or on edge locations supporting both connected and disconnected modes. These components provide the capability to collect data in near real-time from a variety of sources to include streaming and structured/unstructured data from the host’s data sources. The platform provides the tools and mechanisms to support data federation, data ingestion, linkage, and aggregation of information into a knowledge layer for the enterprise providing a scalable data acquisition and integration solution designed for fast transfer of large volumes of data.
Edge and IoT AI/ML support
Additionally this platform provides enterprise-grade built-in tools for enabling full Artificial Intelligence (AI) and Machine Learning (ML) life cycle support. MDACA enables automation of AI/ML life cycle management with life cycle phase-specific tools that improve overall throughput and efficiency of data science activities. Our system is DoD compliant with the DISA STIG security controls and documentation. MDACA’s MLOps Platform provides the software tools supporting the full AI/ML life cycle dedicated to providing a robust infrastructure for making deployments simple, portable, and scalable for ML workflows on Kubernetes. MDACA’s MLOps Platform provides a complete machine learning operations platform that simplifies, accelerates, and secures the machine learning model development life cycle to include retraining that is included in the edge devices.