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Machine Learning and Analytics: Process Engineers Don’t Have to Be a Data Scientist

Turn raw data into real-time analytics with a Process Digital Twin

Machine Learning and Analytics | GE Digital webinar

On-demand webinar

Today, staying competitive means progressing with machine learning and analytics. Fortunately, the journey to success doesn’t require that process engineers need to be data scientists.

 

In this on-demand webinar, you will learn how to align engineering domain expertise to five capabilities:

 

  1. Analysis - automatic root cause identification accelerates continuous improvement
  2. Monitoring – early warnings reduce downtime and waste
  3. Prediction – proactive actions improve quality, stability, and reliability
  4. Simulation – what-if simulations accelerate accurate decisions at a lower cost
  5. Optimization – optimal process setpoints improve throughput at acceptable quality by up to 10%
CSense predictive analytics screenshot, process digital twin software from GE Digital

Proficy CSense

Proficy CSense uses AI and machine learning to enable process engineers to combine data across industrial data sources and rapidly identify problems, discover root causes, and automate actions to continuously improve Quality, Utilization, Productivity, and Delivery of production operations.