Capability
MLOps & Model Lifecycle
The pipelines and routines that keep models current after launch: versioned data and models, automated retraining and evaluation, controlled release and rollback, and cost monitoring. Your team always knows which model is running and why it changed.
Most models lose accuracy as the world they describe changes. Model lifecycle management turns retraining and release into a routine instead of a project.
01Architecture
Reference architecture
A typical arrangement of the components. Every implementation is adapted to your systems, data-residency and security requirements.
Data
- Versioned datasetsDVC
- Feature pipelinesAirflow
Training and evaluation
- Retraining pipelinesAirflow · Docker
- Comparison with the production model
Registry and release
- Model registryMLflow
- Staged rolloutKubernetes · GitHub
Monitoring
- Quality, cost and latencyPrometheus · Grafana
- Retraining triggers
02Components
What this capability covers
01
Versioning and registry
Datasets, features and models versioned together, with a registry that records what is in production.
02
Retraining pipelines
Scheduled or triggered retraining, with automatic evaluation against the current model.
03
Controlled release
Staged rollout, shadow or canary testing, and one-step rollback.
04
Cost and performance tracking
Inference cost, latency and usage monitored alongside model quality.
03Implementation
How we implement it
We establish the lifecycle on one model first and then apply it to the rest, so the process fits your team's capacity. Every change to data, code or configuration produces a traceable model version.
- Promotion rules compare each candidate with the production model on agreed metrics.
- Rollback is a tested, routine operation.
- Access to production models and data follows least privilege.
04Deliverables
What you receive
Lifecycle design and promotion rules
Retraining and evaluation pipelines
Model registry and release process
Monitoring and cost dashboard
Technology
Technologies we work with
- MLflow
- DVC
- Airflow
- Docker
- Kubernetes
- GitHub
- Prometheus
- Grafana
Chosen per project and aligned with your existing standards. Listed as technologies we use, not as partnerships or endorsements.
05Use cases
Typical applications
Patterns this capability is designed for, not a list of delivered projects.
01
Forecast models that follow the market
Demand or price models retrained on new data and released only when they outperform the current version.
02
Vision models for new product variants
Inspection models extended to new product variants without disrupting the line.
03
Language-model applications
Prompt, retrieval and model changes released through the same evaluation gates as code.
06Related
Where to go next
Industry contexts
More in Operate & Scale
- Validation & Reliability
Model evaluation, robustness testing, real-world validation and post-deployment performance monitoring that keep AI dependable in operation.
Next step
Scope MLOps & Model Lifecycle for your organisation
Share your process, data and constraints. We will propose an approach, the evidence to collect first and a realistic plan.