Data · Analytics · AITalk to an expert
All expertise/10
CAPABILITY 10

MLOps & LLMOps

Give models and AI applications a controlled release process and an operating owner.

Discuss this capability What we deliver View in Services catalogue
BUSINESS CONTEXT AND APPROACH

Give models and AI applications a controlled release process and an operating owner.

A production AI service needs a repeatable path from experiment to release. We establish versioning, automated checks and clear responsibilities so teams can update models and prompts without losing traceability.

Model releases and prompt changes can affect accuracy, cost and safety. We design deployment gates around the business risk, connect monitoring to an incident owner and define rollback or retraining steps before launch.

OUTCOME AND MEASURES

Repeatable releases with monitoring, acceptance criteria and a recovery plan.

Before delivery starts we agree baselines for:

  • Release traceability and evaluation coverage
  • Detection and recovery time for quality incidents
  • Quality, latency and spend after deployment
MLOPS & LLMOPS DELIVERY ARCHITECTURE

Version, evaluate, approve, deploy and observe.

A reference delivery architecture for releasing models and prompts through gates sized to the business risk, with monitoring that reaches an owner.

ANIMATED SCENARIO

Release an AI update with control

The scenario follows a prompt change through regression tests, approval, deployment and monitoring with a named owner.

REPRESENTATIVE USE CASES

Use cases shaped
around real work.

Representative patterns are tailored to your landscape, controls, data readiness and business priorities.

01

Model release pipeline

Promote validated models through environments using a registry, test results and release approvals.

02

LLM regression testing

Check whether a prompt, model or retrieval change harms grounded-answer quality before release.

03

Drift and incident response

Connect a quality alert to investigation, retraining or rollback with a documented service owner.

WHAT WE DELIVER

Artifacts your teams can use.

  • Versioned data, prompts, experiments and model registry
  • Automated evaluation and deployment pipelines with release approvals
  • Model drift, response quality, latency and cost monitoring
  • Retraining, rollback procedures and operational runbooks
AGREED BASELINES
01Release traceability and evaluation coverage
02Detection and recovery time for quality incidents
03Quality, latency and spend after deployment
Discuss this capability