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MLOps

MLOps manages the AI model lifecycle in one place, from dataset analysis and training to deployment and monitoring.

AI Model Lifecycle Management

One Flow from Dataset Analysis to Deployment and Monitoring

MLOps brings the AI model lifecycle into one place, from dataset analysis and model training to deployment and performance monitoring. The solution can be adapted to the characteristics of your manufacturing data and the systems where your models will run.

  • Dataset Analysis
  • Model Training and Deployment
  • Performance Monitoring
  • Data Drift
  • Model Improvement
  • Illustration of a wall of blue and white cubes breaking apart into particles

    Keep Track of Model Performance After Deployment

    AI models are trained on historical data, but operating data continues to change as process conditions and raw materials evolve. As this data drift builds, a model that performed well at deployment may become less accurate. MLOps tracks operating data alongside model performance to help teams identify when a review is needed.

  • Illustration of silver cubes traveling through pipes and assembling into a glass cube structure

    Bring Dataset Analysis, Training, Deployment, and Monitoring Together

    Models are trained based on an analysis of dataset characteristics, then deployed to the target system. After deployment, their performance is monitored against actual operating data. Model versions and training history are managed together, helping teams compare potential causes of performance changes and determine how to improve the model.

  • Illustration of three clay-style 3D servers connected to a cloud by cables

    Start with Your Objectives and Deployment Environment

    Before implementation, we review your AI objectives, available data, and target deployment systems. We then define the scope of training, deployment, and monitoring, starting with the capabilities you need. Performance records collected during operation support model improvement and help inform future AI initiatives.

Features & Specs

Features

  • Dataset profiling and analysis
  • AI model training and deployment management
  • Model performance monitoring using operating data
  • Identification of performance degradation associated with data drift
  • Model version and training history management
  • Model improvement informed by performance records

Operating Environment

Applications
Manufacturers and organizations adopting AI
Inputs
Training data, operating data, and AI models
Deployment targets
Servers, edge devices, and other model-serving systems
Management scope
Data analysis, training, deployment, and performance monitoring

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