Models stuck in notebooks
Promising models cannot be deployed because there is no reliable path to production.
05 / Technology & Engineering
Machine Learning Operations
Deploying a model once is not the hard part. Keeping it accurate, reproducible and safe as data, requirements and infrastructure change is. MLOps brings engineering discipline to the machine learning lifecycle so models remain dependable long after the first release.
At a glance
- Service
- Machine Learning Operations
- Pillar
- Technology & Engineering
- Typical starting point
- ML lifecycle maturity review
- Typical first phase
- 2 to 3 weeks for review; core MLOps in 6 to 12 weeks
- Key technologies
- MLOps, Machine Learning, Cloud AI
Durations depend on scope, data readiness and integration needs.
What is MLOps?
Short answer
Machine Learning Operations (MLOps) is a set of practices and tools for reliably developing, deploying, monitoring and maintaining machine learning models in production. It covers data and model versioning, reproducible training pipelines, model registries, automated testing and deployment, performance and drift monitoring, and retraining. LLMOps applies similar practices to applications built on large language models.
Without MLOps, models are often trained on a data scientist's laptop, deployed by hand, never monitored and retrained only when someone notices a problem. That approach might survive one model. It does not survive ten.
We implement MLOps proportionate to your needs. A small team with a few models may need experiment tracking, a model registry, a simple deployment pipeline and monitoring. A larger organisation may need feature stores, automated retraining, approval workflows and platform capabilities for many teams. For LLM applications, the focus shifts to prompt versioning, evaluation pipelines, tracing and cost monitoring.
Business problems
What problems does MLOps solve?
These are the situations where organisations most often find this service worthwhile.
Irreproducible results
Nobody can recreate how a production model was trained.
Silent model degradation
Accuracy declines as data changes, unnoticed until business impact appears.
Manual, risky deployments
Model updates are deployed by hand without testing or rollback.
Growing numbers of unmanaged models
Teams lose track of which models are in use, where and by whom.
LLM prompts changed without testing
Prompt edits affect quality with no evaluation or version history.
Use cases
Where can MLOps be used?
Representative applications. The right use case depends on your processes, data and constraints.
| Area | Example application |
|---|---|
| Experiment tracking | Recording data, code, parameters and metrics for every training run. |
| Deployment pipelines | Automated testing and deployment of models as APIs or batch jobs. |
| Monitoring | Tracking prediction quality, data drift, latency and errors in production. |
| Automated retraining | Pipelines that retrain and validate models on new data with approval steps. |
| LLMOps | Prompt versioning, evaluation pipelines, tracing and cost dashboards for LLM applications. |
| Model governance | Registries with documentation, approvals and audit history. |
Who should consider it
- Data science teams with models ready for production
- Organisations running several models in production
- Teams operating LLM-based applications
- Regulated organisations needing model audit trails
How AARHIT approaches it
How we deliver MLOps
Each stage ends with something you can review, so decisions are made on evidence rather than on promises.
Stage 1 of 6
Maturity review
We review how models are developed, deployed and maintained today, and identify the highest-impact gaps.
Output: MLOps maturity assessment and roadmap.
Stage 2 of 6
Platform design
Tools and processes are selected to fit your cloud, team size, model types and governance needs.
Output: MLOps architecture.
Stage 3 of 6
Reproducible training
Training pipelines with data versioning, experiment tracking and model registry are implemented.
Output: reproducible training workflows.
Stage 4 of 6
Deployment automation
CI/CD pipelines test and deploy models with staged rollout and rollback.
Output: automated deployment.
Stage 5 of 6
Monitoring and alerting
Performance, drift, data quality, latency and cost are monitored with alerts.
Output: model monitoring dashboards.
Stage 6 of 6
Retraining and governance
Retraining triggers, validation gates, approvals and documentation standards are put in place.
Output: operating model for the model lifecycle.
Technology
What technologies are involved?
We choose tools on fit, cost, security and your existing estate. These are typical options, not fixed commitments.
Learn more about the underlying technologies
| Layer | Typical options |
|---|---|
| Tracking and registry | MLflow, Weights and Biases, cloud model registries |
| Pipelines | Kubeflow, Vertex AI Pipelines, SageMaker Pipelines, Azure ML, Airflow |
| Data and features | DVC, lakeFS, Feast and managed feature stores |
| Serving | KServe, BentoML, Triton Inference Server, vLLM, managed endpoints |
| Monitoring | Evidently, WhyLabs, Arize, Prometheus and Grafana |
| LLMOps | Langfuse, LangSmith, promptfoo, evaluation pipelines |
Outcomes
Potential business outcomes
Targets are agreed with you during scoping and measured against a baseline. We do not quote improvement figures before we have seen your process and data.
- Faster, safer path from model development to production
- Reproducible models with full audit history
- Early detection of model degradation
- Lower operational effort per model
- Controlled change for LLM prompts and models
How long does implementation generally take?
Typical timeline
A maturity review usually takes 2 to 3 weeks. Core MLOps capabilities such as tracking, registry, deployment pipelines and monitoring for priority models typically take 6 to 12 weeks. Platform capabilities for many teams are built in further phases.
What determines complexity?
- Number and types of models
- Batch versus real-time serving
- Existing cloud and data platforms
- Regulatory and audit requirements
- Team size and skills
Implementation
Implementation considerations and risks
Most difficulties in this kind of work are predictable. Planning for them early keeps delivery on track.
Avoid over-engineering
A full platform for two models wastes effort. Match tooling to scale and growth plans.
Monitor business outcomes too
Technical metrics may look fine while business results decline. Link monitoring to outcomes where possible.
Ground truth arrives late
For many predictions, actual outcomes are known weeks later. Use drift indicators in the meantime.
| Risk to consider | How we manage it |
|---|---|
| Undetected model drift | Data and prediction drift monitoring with alert thresholds. |
| Faulty models reaching production | Automated validation gates, staged rollout and rollback. |
| Tool sprawl | A deliberate, documented toolchain aligned with existing platforms. |
| Compliance gaps | Model registry with documentation, lineage and approval records. |
FAQ
Questions about MLOps
What does an MLOps engagement usually deliver first?
Usually experiment tracking, a model registry, an automated deployment pipeline and production monitoring for the most important models, followed by retraining pipelines and governance as the number of models grows.
How do you detect model drift in production?
By comparing incoming data distributions with training data, tracking prediction patterns and measuring accuracy against actual outcomes as they arrive, with alerts when agreed thresholds are crossed.
What is the difference between MLOps and LLMOps?
MLOps focuses on training and operating predictive models. LLMOps addresses applications built on large language models, emphasising prompt and version management, evaluation of open-ended outputs, tracing, safety filtering and cost control.
Do small teams need MLOps?
Yes, in proportion. Even one important model benefits from experiment tracking, versioning, a repeatable deployment process and basic monitoring.
Can MLOps work with our existing cloud platform?
Yes. AWS, Azure and Google Cloud all provide MLOps services, and open-source tools can run on any of them or on-premises. We design around your current platform.
Next step
Considering MLOps for your organisation?
Share the process, product or decision you have in mind. We will ask the right questions, outline options and suggest a sensible first step.