ML Service

ML Support Offerings

Experienced team will keep re-evaluating and retraining the models against fresh data over an application’s life. This ensures that the models can continue to do their jobs—such as recognizing faces, predicting events, and inferring customer intentions—with acceptable accuracy


  • Operationalizing Machine Learning and AI for Business Teams
  • Deploy/on-board, execute & manage ML models
  • Monitor, Re-evaluate, Tune and Manage Models on an Ongoing Basis
  • Monitoring  Machine Learning Models:
    • Monitor & evaluate 'model drift'
    • Monitor resource Consumption
    • Monitor cost of Model performance (Records/second)
    • Monitor Models per SLA’s
  • Pipeline Management:
    • Control/Canary Pipeline
    • A/B Testing
    • Detecting Errors
    • Recovery from failures
  • Model Re-training
  • Model rollback
  • Support Performance Metrics
  • Champion-Challenger comparison model to test performance between two, or many, machine learning models
  • Connect data science, data engineering and DevOps in a natural and scalable way
  • Realize the business impact promised by Machine Learning

Application Support

  • Analysis & Troubleshooting 
  • Change Management
  • Release Management
  • Database Support
  • Innovation & Transformation

Service Desk

  • Analysis & Troubleshooting
  • Change Management
  • Release Management
  • Innovation & Transformation

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