01
How are models and application versions promoted?
Define reproducible environments, testing, approvals and release paths from development into production.
AI, automation and optimization
MLOps & LLMOps
Operate models and language-model applications as production systems, with versioning, evaluation and ownership around every change.
A model is not finished when it produces a good notebook result. Production AI requires repeatable deployment, data and model versioning, monitoring, evaluation, incident handling and criteria for retraining or rollback.
MLOps and LLMOps extend those controls across predictive models and language-model applications. The exact toolchain varies, but the operating questions remain consistent: what changed, who approved it, how quality was measured and how the system behaves in production.
NoèmeX perspectiveMake the system intelligible first: reveal the structure, constraints and variables that determine where change can produce the intended outcome.
Related leadership priorities
AI priorities depend on who owns the decision, risk and operating outcome.
Use these pathways to connect AI, automation and optimization work to the executives responsible for governance, foundations and business performance.
Questions we address
The decisions that shape the mlops and llmops work
We start with the business process, decision, information and operating controls around the use case before selecting models, platforms or automation technologies.
02
What should be monitored continuously?
Track data quality, drift, prediction performance, latency, cost, retrieval quality and application-specific evaluation metrics.
03
When should a model be retrained, changed or rolled back?
Establish thresholds and decision rights for corrective action rather than relying on ad hoc intervention.
04
How are incidents and model changes audited?
Preserve logs, lineage, evaluation evidence, ownership and deployment history for operational accountability.
What we deliver
A production-minded capability, not an isolated experiment
The exact scope depends on the organization and use case, but the work typically produces a combination of these outputs.
01
Model operations architecture
Environments, registries, pipelines, deployment patterns and monitoring components for production AI.
02
CI/CD and release controls
Automated testing, evaluation, approval and promotion processes for model and application changes.
03
Monitoring and evaluation
Technical and business performance metrics, drift, quality, latency, cost and alerting.
04
Versioning and lineage
Traceability across code, data, models, prompts, retrieval configuration and deployed applications.
05
Incident and rollback procedures
Runbooks, ownership, escalation, rollback and recovery paths for production failures.
06
Lifecycle management
Retraining, re-evaluation, model replacement, retirement and continuous-improvement procedures.
Relevant model-operations evidence
Evidence and operating context from enterprise transformation work
The production-optimization engagement documents a governed environment for deploying and sharing optimization algorithms across regions. It supports the MLOps operating principles on this page; the portfolio does not separately document an LLMOps implementation.
Algorithm deployment environmentGlobalEurope, Middle East and APAC · Shared optimization platform
Creating a governed platform for deploying algorithms at multinational scale
Engineers needed to deploy and reuse optimization algorithms across plants and regions rather than run them locally. NoèmeX created a centralized platform with shared compute, a deployment and knowledge-sharing framework, usage analytics, governance for platform evolution and embedded operational support. This is direct evidence of production algorithm operations; it is not represented as an LLMOps case.
Read the related caseRelated services
Continue into the adjacent capability.
AI, automation and optimization often intersect with data, analytics, process and governance work. These pages keep each capability focused while making those relationships explicit.
How we work
Use the engagement model that fits the problem.
Work can begin as focused advisory and continue into implementation, embedded support, managed operations or run-and-maintenance depending on the maturity and criticality of the capability.
Project delivery
Build deployment pipelines, environments, monitoring, evaluation and release controls.
Read more →Run & maintenance
Operate monitoring, incidents, model changes, retraining and platform continuity.
Read more →Your Contact Persons
Connect directly with the people responsible for this work. Explore their experience and contact options.
Zahra Fathisalout
Founder · Eng., CSP, CMLAI
Corporate governance · ASC-C.Dir. candidate
Works across strategy, enterprise transformation, data, AI and governance, bringing operational engineering experience to complex transformation decisions.
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Brendan Young
B.Com, Director of Operations
Connects transformation design with operating discipline, drawing on logistics, fulfillment and high-throughput operational leadership.
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MLOps & LLMOps
Start with the business problem, the workflow and the decision that needs to improve.
Share the models or AI applications, deployment environment, current release process, monitoring gaps and operational responsibilities. Our teams can design the production lifecycle and controls required for dependable AI operations.