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ML Model Migration & Monitoring

Keep your ML models performing, no matter where they run. We handle migration, optimisation, and monitoring.

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ML Model Migration & Monitoring

What we can do

Migrate ML models across platforms without losing performance.

We optimise for speed, accuracy, and cost. We set up governance for versioning, lineage, and compliance. We monitor for drift, bias, and model health so you catch issues before they hurt.

“We handle the full ML lifecycle: migrate your models, optimise them for production, set up governance, and monitor for drift and bias so they stay reliable.”

System flow / ML Model Migration & Monitoring

Model lifecycle

Track model identity, performance, and governance as a model moves into production and through ongoing monitoring.

01 / MOVE / Migrate model

Move between SageMaker, Azure ML or Vertex AI

How We Manage Your ML Lifecycle

Migrate models without performance loss. Optimise for speed, accuracy, and cost. Set up governance for versioning, lineage, and compliance. Monitor for drift, bias, and model health.

  1. 01

    Cross-Platform Migration

    • Cross-platform migration
    • SageMaker / Azure ML / Vertex AI
    • Version control & lineage
  2. 02

    Model Optimisation

    • Model optimisation: pruning, distillation, quantisation
    • Domain-specific fine-tuning
    • No retraining from scratch
  3. 03

    Governance & Risk

    • Governance aligned with DAMA and NIST
    • Audit trails & access control
    • Enterprise risk policies
  4. 04

    Drift & Bias Monitoring

    • Drift detection & bias monitoring
    • Real-time dashboards
    • Automated alerts

Scope, made tangible

Explore the work areas

Select a work area to see what it covers, including the technologies used for it. Scope and work products are tailored to your environment.

AREA 01

Model Migration

Move ML models across SageMaker, Azure ML, and Vertex AI without losing performance or data.

  • Cross-platform migration
  • Data lineage preservation
  • Zero performance loss
  • SageMaker
  • Azure ML
  • Vertex AI

Delivery path / ML Model Migration & Monitoring

One delivery framework, tailored to this system.

Each phase is shaped around the systems, controls and operating needs of this service.

  1. 01 PHASE

    Platform & Lineage Review

    Review model platforms, versions, lineage and performance needs.

    Check / output

    Model inventory and scope

  2. 02 PHASE

    Cross-Platform Optimisation

    Migrate and optimise models; configure MLOps and CI/CD.

    Check / output

    Cross-platform model checks

  3. 03 PHASE

    Speed, Accuracy & Cost Comparison

    Deploy the model and compare speed, accuracy and cost.

    Check / output

    Performance review

  4. 04 PHASE

    Versioning & Access Records

    Apply versioning, lineage, access controls and audit trails.

    Check / output

    Governance records

  5. 05 PHASE

    Drift, Bias & Health Watch

    Monitor drift, bias and health, with dashboards and alerts.

    Check / output

    Monitoring dashboards

Engagement outputs

Typical work products

Final outputs are agreed during discovery, based on your environment and the work in scope.

  • PANEL 01

    Model migration

    A cross-platform move with performance and lineage checks.

  • PANEL 02

    Version and lineage controls

    Model tracking, audit trails and access controls.

  • PANEL 03

    Model optimisation

    Pruning, distillation, quantisation or fine-tuning.

  • PANEL 04

    Production monitoring

    Dashboards and alerts for drift, bias and health.