MLOps and LLMOps
Models that keep performing long after launch.
Agleam Labs builds the pipelines and platforms that take machine learning and LLM applications from experiment to reliable production. We automate training, testing, deployment, and monitoring, so your models stay accurate and your team can ship changes with confidence.
- Automated training and deployment
- Model registry and versioning
- Monitoring, drift, and evaluation
- LLMOps for prompts and agents
What we do
DevOps discipline for models and AI applications.
Training a good model is only the start. In production, data shifts, upstream systems change, and a model that was accurate in testing slowly degrades. Without proper pipelines, retraining is manual, deployments are risky, and nobody can say exactly which model version made which decision. MLOps fixes this with automation, versioning, and monitoring.
LLM applications bring their own operational needs: prompt versioning, evaluation suites, cost and latency tracking, and safe rollout of model upgrades. We apply the same engineering rigour to both, drawing on our team's long DevOps experience to build platforms that fit your cloud and the tools your data scientists already use.
What we deliver
The full model lifecycle, automated.
We set up the foundations once and make every future model and AI feature faster and safer to ship.
01 ML pipelines and automation +
- Training pipelines
- Orchestration
- Reproducibility
- Scheduling
Reproducible pipelines for data preparation, training, validation, and packaging, triggered on schedule or when new data arrives.
02 Model registry and versioning +
- Model registry
- Lineage
- Experiment tracking
- Approvals
A central record of every model, its training data, parameters, and evaluation results, with approval stages before promotion to production.
03 Deployment and serving +
- Model serving
- Canary releases
- Shadow testing
- Autoscaling
Models served as scalable APIs or batch jobs, with canary and shadow releases so new versions are proven before they take full traffic.
04 Monitoring and drift detection +
- Data drift
- Accuracy tracking
- Alerting
- Dashboards
Tracking of input data, prediction quality, latency, and errors, with alerts when performance drops or data starts to look different.
05 Automated retraining +
- Continuous training
- Champion-challenger
- Sign-off
- Rollback
Retraining triggered by drift or schedule, with automatic comparison against the live model and human sign-off before replacement.
06 LLMOps and evaluation +
- Prompt versioning
- Eval suites
- Tracing
- Model upgrades
- Guardrails
Versioned prompts, automated evaluation suites, tracing of LLM and agent calls, and controlled rollout of new model versions and providers.
07 Cost and performance optimisation +
- GPU utilisation
- Caching
- Model routing
- Cost reporting
Right-sized infrastructure, GPU scheduling, caching, and model routing to keep AI running costs predictable as usage grows.
08 ML platform engineering +
- Infrastructure as code
- Kubernetes
- Feature stores
- Notebooks
Self-service platforms on your cloud, defined in code, that give data scientists the environments and tools they need without waiting on operations.
Why MLOps
Ship models faster, with fewer surprises.
-
Faster releases
New and updated models move to production in a routine, automated step.
-
Accuracy maintained
Drift is caught early and models are retrained before results suffer.
-
Full traceability
Know which model version, data, and code produced every prediction.
-
Safe rollouts
Canary releases and instant rollback make change low-risk.
-
Controlled costs
Clear visibility of compute and API spend, with optimisation built in.
-
Struggling to keep models reliable in production? Book a free consultation and we will review your set-up.
Book a free consultation →
Why Agleam Labs
MLOps from a team with DevOps roots.
Hover or tap a card to see what it means in practice.
How we work
From manual hand-offs to automated operations.
-
01
Assess
We review how models are built, deployed, and monitored today, and identify the biggest risks and bottlenecks.
-
02
Design the platform
We agree the target architecture, tooling, and workflows to fit your cloud and team.
-
03
Automate
We build pipelines, registry, serving, and monitoring, migrating existing models one at a time.
-
04
Hand over and support
We train your team, document everything, and provide ongoing support as needed.
Agleam Labs in numbers
- Enterprise apps delivered
- 50+
- Enterprise apps delivered
- On-time delivery
- 99.8%
- On-time delivery
- Years of senior experience
- 10–30
- Years of senior experience
- Years of DevOps experience
- 20+
- Years of DevOps experience
- Technologies in production
- 20+
- Technologies in production
- Office, worldwide clients
- 1
- Office, worldwide clients
Technology
MLOps and LLMOps tooling.
Open-source and cloud-native tools, chosen to fit your environment and avoid unnecessary lock-in.
- MLflow
- Kubeflow
- Airflow
- SageMaker
- Azure ML
- Vertex AI
- Langfuse
-
Python
-
Kubernetes
-
Docker
-
Terraform
-
GitHub Actions
-
CI/CD
- Prometheus
- Grafana
-
Monitoring
Industries
Reliable AI operations for every sector.
Wherever models drive real decisions, they need production-grade operations behind them.
FinTech
Payment, lending, and transaction platforms for always-on financial operations.
E-Commerce
Commerce technology connecting customers, inventory, payments, and retail operations.
Telecom
Digital platforms, integrations, and service systems for continuous delivery.
Healthcare
Secure digital systems for data-heavy healthcare workflows and service delivery.
Manufacturing
Industrial software that connects production, inventory, quality, and plant operations.
Technology and SaaS
SaaS platforms and AI products built to scale with users, features, and integrations.
FAQs
MLOps questions.
Straight answers about mlops with Agleam Labs.
Talk to an expert
Tell us what you are working on. A senior engineer will reply with honest advice and a suggested next step.
Book a call →What is the difference between MLOps and LLMOps? + −
MLOps covers the lifecycle of trained predictive models: data, training, deployment, and drift. LLMOps covers applications built on language models: prompts, evaluation, tracing, cost, and provider upgrades. Many organisations need both.
We only have a few models. Do we need MLOps? + −
Even one important model benefits from versioning, monitoring, and a repeatable deployment. We scale the set-up to your needs rather than building a heavy platform you will not use.
Can you work with models our team already built? + −
Yes. We regularly take existing notebooks and models and move them into proper pipelines, without changing the modelling approach unless you want us to.
Which cloud platforms do you support? + −
AWS, Azure, and Google Cloud, as well as on-premise Kubernetes. We use managed ML services or open-source tools depending on your preferences and constraints.
How do you evaluate LLM applications? + −
We build test sets from real usage, score outputs automatically for accuracy, format, and safety, and run these checks on every prompt or model change before release.
How long does an MLOps engagement take? + −
An assessment takes two to three weeks. Building the core platform and migrating a first model typically takes eight to fourteen weeks, depending on scope.
Start a project
Let's make your models production-ready.
- A free consultation with a senior engineer
- Clear scope, timeline, and budget
- Confidential, with an NDA on request
- Support after delivery
Prefer email?
info@agleamlabs.comRun AI in production with confidence.
Tell us how you build and deploy models today. We will suggest the improvements that will make the biggest difference.