XMANAI On-premise execution and visualization environment: A Deep Dive into the Final Release Version
The XMANAI platform offers a cutting-edge approach to Explainable Manufacturing Artificial Intelligence and has recently unveiled its Beta Version, building upon the solid foundation laid by the Alpha release. As has been presented in the past, the XMANAI Platform architecture comprises three core tiers, namely: a) the XMANAI Cloud infrastructure which constitutes the core part of the platform and represents the centralized cloud instance of the XMANAI Platform, b) the XMANAI On Premise Environments which represent the parts of the XMANAI Platform that can be hosted and executed in a private cloud instance of a stakeholder and c) the XMANAI Manufacturing Apps Portfolio which is composed of AI manufacturing intelligence solutions that are effectively solving specific manufacturing problems. The XMANAI On Premise Environment facilitates the execution of the platform’s functionalities on the stakeholders’ environments based on the instructions that are provided by the XMANAI Cloud infrastructure in accordance with the preferences of the stakeholders. This blog post delves into the intricacies of the on-premise execution and visualization environment of the XMANAI platform’s Final Version release, highlighting the enhancements and new features that set it apart.
On-premise Execution: Enhanced Flexibility and Control
The Final Version of XMANAI continues to support on-premise execution, a crucial feature for organizations prioritising data security and operational autonomy. This execution mode allows users to leverage the full suite of XMANAI’s AI and XAI pipelines within their infrastructure, ensuring data privacy and compliance with internal policies.
Key aspects of the on-premise execution include:
Data Handling and Processing: The platform facilitates seamless data import and manipulation, allowing users to integrate their data sources effortlessly.
Execution & Orchestration Engine: This engine is pivotal in managing the execution of XAI pipelines, either on a predefined schedule or on demand, ensuring flexibility in operational workflows.
Visualization Environment: Improved clarity and Insight. The visualization aspect of the XMANAI platform has been meticulously designed to offer users a comprehensive view of their AI and XAI operations. This environment is not just about presenting data in a meaningless and confusing manner, it’s about making them understandable and actionable.
Features of the visualization environment include:
XAI Insights Workflow: This workflow is central to the visualization environment, enabling users to gain AI insights in an explicit and understandable manner. It integrates components like the XAI Pipeline Manager and the XAI Visualisation Engine.
User-Centric Design: The platform’s visualization tools are tailored to provide business users with a clear understanding of AI processes, facilitating informed decision-making throughout their manufacturing processes.
The Anonymiser: A notable feature in the on-premise environment is the Anonymiser. This tool offers offline data anonymization capabilities, allowing users to preprocess datasets locally for enhanced privacy before uploading them to the XMANAI platform.
Seamless Integration and User Experience
The Final XMANAI platform release enhances existing features and introduces optimizations across different perspectives and abstraction levels. The updated user journeys document the interactions among the platform components, showcasing an integrated and user-friendly experience.
Conclusion
The XMANAI platform marks a significant step forward in the realm of explainable AI and in particular in manufacturing. With its robust on-premise execution capabilities and advanced visualization environment, it offers users an unparalleled level of control, insight, and security. As XMANAI continues to evolve, it remains committed to addressing user needs and refining its features, paving the way in the industry.
