Model deployment and serving
We containerize and deploy models with inference servers, autoscaling, and request batching that make model endpoints behave like production services — not research experiments.
Service 06
AI & model engineering
AI and model engineering services for teams that can train a model — and are struggling to run it reliably in production.
Why this service
Most teams can get a model working in a notebook. Getting it into production reliably is the hard part.
The gap between a working model and a production model is mostly operational: no repeatable training pipeline, no deployment controls, no drift detection, no audit trail. Models degrade silently, retraining is manual, and governance is an afterthought. This service builds the infrastructure that makes model development repeatable and model operations trustworthy.
Focus areas
What this service covers.
MLOps and observability
We build training pipelines, experiment tracking, and artifact lineage that give ML teams reproducibility and full audit trails from experiment to deployment.
Model governance and monitoring
We implement drift detection, performance monitoring, and policy controls so models stay accurate, fair, and auditable — not just at launch, but over time.
Detailed offerings
Service modules for architecture, platform, and execution.
Each module can run independently or as part of a larger modernization program.
Model serving and inference platform design
We design and implement robust model-serving architecture with scalability, reliability, and cost controls.
- Inference service architecture for real-time and batch workloads
- Autoscaling, request routing, and response-latency optimization
- Versioned model endpoint strategy with rollback safety
MLOps workflow and pipeline engineering
We establish repeatable pipelines for data preparation, training, validation, and deployment with full traceability.
- Experiment tracking and reproducibility standards
- Artifact lineage and model registry governance
- Automated training-to-deploy workflows with approval controls
Model quality and drift monitoring
We implement production-grade monitoring so model behavior is continuously measured and governed.
- Performance and drift metrics by segment and use case
- Data-quality and feature-distribution monitoring
- Alerting and retraining triggers linked to model health thresholds
Governance, risk, and compliance controls
We integrate governance and policy controls into the model lifecycle for regulated and high-impact use cases.
- Approval workflows and model risk classification
- Audit trail design across data, model, and deployment decisions
- Policy controls for explainability, fairness, and access management
AI product integration and execution
We help teams embed model capabilities into product workflows with operational realism and measurable outcomes.
- Use-case prioritization by feasibility and business impact
- API and product integration patterns for model-powered features
- Operating model for collaboration between data science and engineering
Engagement models
Ways we deliver this service.
Choose a delivery format that matches urgency, scope, and internal capacity.
AI readiness and architecture assessment
A focused engagement to evaluate current model operations, risk posture, and production readiness gaps.
MLOps and serving foundation program
A build phase to establish model serving, training pipelines, governance controls, and monitoring standards.
Scale and governance acceleration
Embedded partnership to scale model operations across teams, use cases, and production environments.
What you receive
Concrete deliverables, not generic recommendations.
Every engagement ends with artifacts your teams can execute and maintain.
- Production model-serving architecture and deployment standards
- MLOps pipeline design with lineage, registry, and release controls
- Monitoring framework for drift, quality, and operational health
- Governance model for approvals, risk controls, and auditability
- Integration blueprint for model features in product systems
- Phased roadmap from pilot to scaled production operations
Target outcomes
Business and engineering impact we optimize for.
2-3x
Faster model release cycles
Standardized pipelines and model registry controls reduce friction between experimentation and production deployment.
35%+
Reduction in model incidents and regressions
Continuous monitoring and governed rollout patterns improve production stability and model reliability.
High
Governance confidence at scale
Traceability and policy controls support audits, compliance requirements, and responsible AI operations.
Common questions
How this engagement works in practice.
Is this only for GenAI use cases?
No. The service covers classical ML, deep learning, and GenAI workloads where production reliability and governance matter.
Can this integrate with our existing data platform?
Yes. We design pipelines and serving workflows around your current data, cloud, and platform architecture.
Do you support governance in regulated industries?
Yes. We implement controls for traceability, approvals, monitoring, and audit evidence aligned to regulated delivery contexts.
Other services
More ways Karman can help.
Ready to engage?
Start with the problem. We'll take it from there.
Platform reviews, architecture consulting, or a scoping conversation — we scope engagements quickly.