eXponence for EcoStruxure™

Automated Machine Learning for Predictive Analytics
Published by Quartic.ai

Overview

Easy to Use Automated Machine Learning for manufacturing subject matter experts

When implementing machine learning (ML) solutions for predictive maintenance, Energy, Process, and production efficiency, you are likely to face these challenges:

  • Deploying machine learning solutions requires extensive data science expertise and coding
  • Implementing PoCs and pilot projects can be expensive and slow
  • Scaling successful pilots to online enterprise applications is difficult

The solution

eXponence™ uses automated ML, to enable the industrial users to combine their Ecostruxure data fabric with their domain knowledge to easily build ML applications

Built for industrial subject matter experts, it enables them to use problem solving workflows and highly interpretable ML that increases adoption and trust of industrial AI

By combining ML with the Ecostruxure real-time control and business logic, actionable AI intelligence can be consumed in Ecostruxure

Technology Partner

eXponence for EcoStruxure™

Benefits

"Glass box AI" - Human in the Loop

eXponence with Ecostruxture automates the Data science in Machine learning but lets the user determine the features used to build models, understand and explain the output of models at every step of building, validation and deployment. This accelerates trust and confidence.

Simplicity

Specifically built by and for industrial users, eXponence looks, feels and behave like your existing automation and asset management systems. Familiar OT UI and industrial workflows allow for easy adoption and integration with your existing Ecostruxure investments.

Scalability

The platform is built on the proven enterprise scale Apache foundation of Kafka, Spark, Hadoop and MLFlow. Scaling from a single machine to the enterprise can be done with cost and robustness certainty. The stack has been stress tested with millions of data points and thousands of models to deploy on AWS, Azure, GCP, private cloud or on-premise

Speed

Zero state starts without past failure and labelled data are slowing down valuable projects. A generalized baselining algorithm that works without past failure data, pre-designed workflows for common manufacturing applications, and automated data preparation, cleaning, aggregation and model building means you enrich your Ecostruxure system with intelligence quicker.
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