ReliVision’s Differentiating Features

• High accuracy in a highly variable production setting:

  The unique infrastructure that ReliVision is built upon, enables fast and effective adaptation of AI solutions to use case variations, e.g. product customizations which is becoming a standard rather than a specialized service in today’s global markets. In essence, the ability to retrain and deploy your existing AI solutions with additional data with ease and without any additional investment allows our customers to retain high accuracy levels across a wide portfolio.

• Shorter time to value with low-cost maintenance:

  All AI based automated inspection systems have a learning curve until they achieve sufficient accuracy and reliability in the field. This learning curve is what determines the time-to-value. ReliVision’s unique infrastructure closes the loop of data acquisition, AI model training, deployment, monitoring field operations, getting feedback from the field, retraining and re-deploying AI solutions optimally, which result in a steep learning curve and thus shorter time-to-value. The whole process does not require in-depth AI know-how or coding.

• Cost effective scale up in multiple dimensions to fit to changing needs and constraints:

  The proprietary infrastructure has a microservices based architecture which makes ReliVision scaleable by design and without any extra cost. While the ReliTrainer and ReliAudit modules can handle multiple use-cases simultaneously through a queueing mechanism, they can easily be scaled to multiple production lines and/or plants replicating successful AI solutions. The computation power can be scaled simply by adding new hardware without new software acquisitions or installations.

• Specialized edge modules for production & manufacturing industries:

  ReliVision platform’s ReliAudit module provides endpoints which makes systems integration easy and flexible, together with an intuitive web based UI for monitoring and maintenance. These features together with guaranteed system up-times, on-prem and/or cloud installation with seamless integration, scalability by design in multiple dimensions makes the solution of choice to resolve the procedural and managerial bottlenecks in production and manufacturing industries that hinder value creation with modern AI technologies. 

The Modules & The Architecture

The Relimetric’s ReliVision platform is built upon a modular, flexible and scalable proprietary architecture as depicted below. The main building blocks of the ReliVision architecture are the Relimetrics Training Engines (RMTEs) and the Relimetrics Inference Engines (RMIEs). The whole architecture is based on a distributed micro-services concept. This allows all components to be deployed locally (on-prem) and/or remotely (on cloud), also scaled up easily.
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The 3 main modules of ReliVision are ReliUI, ReliTrainer (built on RMTEs) and ReliAudit (built on RMIEs):

• ReliUI  is  the ReliTrainer frontend for users to access and use ReliTrainer’s data curation and AI powered solution designing, (re)training, testing and deploying functions. 

• ReliTrainer is the data curation and AI powered solution design, (re)train, test and deployment component built on RMTEs. Its functions are accessible via ReliUI. ReliTrainer comes with ReliBoard, a web interface to manage user accounts, to monitor ReliTrainer processes and datasets by means of user friendly dashboards.

• ReliAudit is the shop-floor/field component on which multi-modal data is collected, automation systems are controlled, AI solutions are deployed and monitored. It is built on RMIEs. It comes with a web HMI which has access to the local DB to review audit results and provide feedback for AI model improvement.

ReliVision Ecosystem Architecture

The ReliVision architecture is scalable-by-design in multiple dimensions. In the multi-site scaling dimension, the RMTEs and RMIEs can be multiplied as required, both locally and remotely. In multi-usecase scaling dimension, both RMTEs and RMIEs can handle multiple training and inferencing tasks simultaneously by means of an internal queueing system. In hardware scaling dimension, RMTEs and RMIEs can be easily connected to additional GPUs/CPUs, while RMIEs can also receive input from multiple data sources (eg. cameras). This scalability-by-design brings in ease and cost efficiency for multiplying the value generating shop-floor automated inspection operations.

The comprehensive user account management allows defining custom user profiles with variable access rights. The communication between the ReliUI and RMTEs/RMIEs goes through secure VPN connections when all servers are private to the customer, either on-prem or on-cloud. In all other cases where access to an external server, such as one of Relimetrics servers, is required, HTTPS connections are used to ensure security.  The shop-floor operations are usually time-sensitive hence it is advised to have RMIEs to be deployed on on-prem servers directly connected to the data sources (sensors, cameras, etc.) with 10 Gbs LAN connections.

ReliVision System Requirements

Relivision platform modules can be deployed on-prem or on-cloud. Typically, ReliTrainer is deployed on-prem or on-cloud depending on customer requirements, likewise for ReliAudit but it is almost always deployed on-prem to assure swift shop-floor operation and control. The minimum system requirements are as follows:

• Client Computer for ReliUI

  • OS: Windows 10+

  • HDD: 100+ GB

  • RAM: 64+ GB

• ReliTrainer Server (RMTE) (Cloud + On-prem)

  • OS: Ubuntu 20+ / Windows 11

  • GPU: Nvidia GPU with minimum 16GB dedicated VRAM and tensor cores (eg. lowend - T4 GPU; highend - A100 recommended)

  • HDD: 100+ GB

  • RAM: 64+ GB

  • Connectivity:

         Static IP

         Accessible through 3 ports

• ReliAudit Server (RMIE) (On-prem)

  • OS: Ubuntu 20+

  • GPU (optional for fast runtime): Nvidia GPU with minimum 16GB dedicated

    VRAM and tensor cores (eg. lowend - T4 GPU; highend - A100 recommended)

  • HDD: 100+ GB

  • RAM: 64+ GB

  • Connectivity:

         Static IP

         Accessible through 3 ports

ReliVision platform’s infrastructure is sensor/camera agnostic. The current ReliVision platform has been configured to work with Basler GigE cameras (https://www.baslerweb.com/en/)