Scaling data science
Moving many models from notebooks to production reliably.
Technology / Data and Operations
MLOps
Building a model is only the beginning. MLOps is how organisations keep models reproducible, deployable, monitored and improving, so AI continues to work after the launch announcement.
What is MLOps?
Short answer
MLOps (machine learning operations) is the set of practices, processes and tools that apply software engineering and DevOps principles to machine learning, covering data and model versioning, reproducible training pipelines, testing, deployment, monitoring for performance and drift, retraining and governance of models in production.
Machine learning systems differ from ordinary software because their behaviour depends on data as well as code. A model can degrade without any code change simply because the world changes. MLOps addresses this with practices for tracking experiments, versioning data and models, automating training and deployment, and monitoring predictions and inputs in production.
LLMOps extends these practices to applications built on large language models, where the priorities include prompt and configuration versioning, evaluation of open-ended outputs, tracing multi-step calls, safety filtering and cost control.
Related concepts
- Model registry
- Drift monitoring
- CI/CD
- Feature store
- LLMOps
- Data Engineering
- Cloud AI
How it works
How does MLOps work?
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Track experiments
Code, data versions, parameters and metrics are recorded for every training run.
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Register models
Approved models are stored with metadata, lineage and documentation.
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Automate deployment
Pipelines test models and deploy them with staged rollout and rollback.
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Monitor
Prediction quality, input drift, latency, errors and cost are tracked.
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Retrain and govern
Models are retrained when needed, validated and approved before replacement.
Applications
Where is MLOps useful?
Common applications across industries. Suitability always depends on the specific data, process and constraints.
Regulated model management
Audit trails and approvals for models affecting customers.
Real-time model serving
Low-latency APIs with autoscaling and monitoring.
Automated retraining
Keeping forecasts and risk models current.
LLM application operations
Prompt versions, evaluations, tracing and cost dashboards.
Edge model updates
Managing model versions deployed to devices.
Limitations
Limitations and when not to use it
Every technology has boundaries. Knowing them early avoids expensive disappointment.
- Full MLOps platforms can be excessive for a few models.
- Tool sprawl adds complexity without clear standards.
- Ground truth for monitoring often arrives with delay.
- MLOps cannot compensate for poor problem framing or data.
- Organisational ownership is as important as tooling.
Common misconceptions
MLOps is just DevOps for models.
It builds on DevOps but adds data versioning, experiment tracking, drift monitoring and model governance.
MLOps requires a large platform team.
Small teams can adopt lightweight practices proportionate to their needs.
Monitoring accuracy is enough.
Input drift, latency, cost, fairness and business outcomes also need tracking.
Comparison
DevOps, MLOps and LLMOps
Related disciplines with different emphases.
| Aspect | DevOps | MLOps | LLMOps |
|---|---|---|---|
| Main artefact | Application code | Models, data and code | Prompts, models, retrieval and configuration |
| Testing | Unit and integration tests | Data and model validation | Evaluation sets, safety and grounding checks |
| Monitoring focus | Uptime, errors, performance | Accuracy, drift, data quality | Output quality, cost, latency, safety |
| Change triggers | Code commits | New data, drift, code | Prompt edits, model updates, knowledge changes |
Decision guide
How to evaluate MLOps for your organisation
Questions worth answering before investing. They form the basis of our feasibility and proof-of-concept work.
| Question to ask | Why it matters |
|---|---|
| How many models do we run, and how many are planned? | Determines the appropriate scale of tooling. |
| Can we reproduce any production model today? | Reproducibility gaps indicate urgent needs. |
| How would we know a model had degraded? | Monitoring is often the most valuable first step. |
| Who approves model changes? | Governance requirements shape workflows. |
| Which platforms do we already use? | Aligning with existing cloud tools reduces overhead. |
AARHIT services
Services that apply MLOps
How we put this technology to work for organisations.
Technology & Engineering
Machine Learning Operations
Version, deploy, monitor and retrain models with repeatable pipelines, so they stay accurate after launch.
Explore service Technology & EngineeringCloud AI Infrastructure
Design compute, storage, networking and GPU capacity for training and inference with cost and security controls.
Explore service ResearchAI Model Evaluation and Optimization
Measure model quality, bias, robustness, latency and cost, then tune models and prompts against clear criteria.
Explore service AutomationDevOps and Infrastructure Automation
Automate build, test, deployment and infrastructure provisioning so software reaches production reliably.
Explore serviceRelated technologies
Industries
FAQ
MLOps FAQs
What is MLOps?
MLOps is the practice of reliably deploying, monitoring and maintaining machine learning models in production using engineering principles such as versioning, automation, testing and monitoring.
Why do models need monitoring?
Because real-world data changes after deployment, which can reduce model accuracy without any code change. Monitoring detects this drift early.
What is a model registry?
A model registry stores trained models with versions, metadata, evaluation results, lineage and approval status, acting as the source of truth for which models are in use.
What is LLMOps?
LLMOps is the adaptation of MLOps practices to large language model applications, focusing on prompt versioning, evaluation of generated outputs, tracing, safety and cost management.
When should we invest in MLOps?
As soon as a model supports real decisions. Start with essentials such as tracking, versioning, deployment automation and monitoring, then expand as the number of models grows.
Next step
Exploring MLOps?
Tell us what you want to achieve. We will help you judge whether this technology is the right fit and how to test it with your own data.