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Iknow
enesfrar

MLOps & AI Operations.

Keep your AI systems performing reliably after deployment — with the model performance monitoring, drift detection, retraining pipelines, and operational management that prevent AI systems from degrading silently after go-live.

Artificial Intelligence Services

The Situation — When Clients Come to Us

Organizations come to us for MLOps and AI operations when:

  • AI models have been deployed but are not being monitored — creating production failures, accuracy degradation, and model drift that erodes AI performance and trust without anyone noticing until the business impact is visible
  • Data science teams spend more time managing the ML lifecycle — deployment, monitoring, retraining, and troubleshooting — than developing new AI solutions, because MLOps practices and tooling have never been formalized
  • An AI program is scaling from a handful of models to dozens or hundreds — and the ad-hoc deployment and monitoring approaches that worked at small scale are creating unmanageable operational complexity
  • Organizations operating AI in regulated environments where documented model performance monitoring, drift controls, retraining procedures, and change management are required by regulation or internal audit

What We Do — Our Approach

  1. // Phase 1

    MLOps Maturity Assessment

    We assess the current MLOps maturity — evaluating model deployment processes, monitoring capabilities, retraining cadence, tooling, incident management, and the governance controls required for responsible AI operations — and identify the highest-priority gaps to address.

  2. // Phase 2

    MLOps Architecture Design

    We design the MLOps architecture — ML pipeline automation, model registry, feature store design, monitoring and alerting architecture, retraining trigger logic, and the CI/CD practices for ML that enable reliable, reproducible model deployment at scale.

  3. // Phase 3

    Monitoring & Drift Detection Implementation

    We implement model performance monitoring — data drift detection, concept drift detection, prediction quality monitoring, fairness metric tracking, and the alerting system that escalates performance degradation before it becomes a business incident.

  4. // Phase 4

    Retraining Pipeline & Automation

    We design and implement automated retraining pipelines — defining retraining triggers, data refresh processes, model validation checkpoints, staged rollout procedures, and the approval gates that ensure model changes are governed before production deployment.

  5. // Phase 5

    AI Operations Model & Managed Services

    We design the AI operations model — defining the operational roles, responsibilities, escalation processes, and governance cadence that sustain AI system reliability. Where the organization requires ongoing operational support, Iknow provides managed AI operations services on a retainer basis.

What You Get — Deliverables

  • MLOps maturity assessment and gap analysis
  • MLOps architecture design — pipelines, model registry, feature store, monitoring, and CI/CD for ML
  • Model performance monitoring implementation — data drift, concept drift, prediction quality, and fairness metrics
  • Alerting system configuration for performance degradation escalation
  • Automated retraining pipeline — triggers, data refresh, validation, and staged rollout
  • Model governance controls — change management, approval gates, and audit logging for regulated environments
  • AI operations model — roles, responsibilities, escalation processes, and governance cadence
  • Managed AI operations services (retainer basis, where applicable)

AI that works in the demo is not the same as AI that works in production twelve months later.

Iknow's MLOps and AI operations practice keeps your AI systems accurate, governed, and reliable — from first deployment through long-term operation.

Contact Iknow