Scale and operate
Run AI in production with tests, releases and monitoring
We build the engineering lifecycle around machine learning models and LLM applications: reproducible training, versioned prompts, evaluation, release controls and monitoring.
In short
MLOps and LLMOps move AI systems from experiment to controlled production. Models, prompts, datasets and retrieval settings are versioned, automated quality gates run before release, rollout is staged with rollback, and drift, latency, cost and failures are monitored after launch.
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01 The problem
What problem does this solve?
An AI prototype that worked on someone's laptop is hard to run again, repeat or explain. Nobody can say which data, prompt or model version produced a result, so a drop in quality is hard to trace.
Models, prompts and data keep changing, and quality can slip without anyone noticing. Without evaluation, release controls and monitoring, every change carries risk, and what each answer costs is a surprise.
02 What we build
What do we build?
- Reproducible training and experiment pipelines
- Versioning of datasets, models, prompts and retrieval settings
- Automated quality and regression evaluation
- Release gates with staged rollout and rollback
- Model gateways and serving infrastructure
- Monitoring of drift, quality, latency and token cost
- Tracing and failure analysis for debugging
03 Architecture
What does a typical architecture look like?
04 How it goes
How does the work usually go?
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Take stock of your AI systems
We list the models, prompts, datasets and services in use, how each is released today, and where quality or cost is not measured.
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Version and reproduce
We put training, datasets, models, prompts and retrieval configurations under version control, so any result can be reproduced and traced.
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Build evaluation and gates
We assemble evaluation datasets from representative cases and add automated quality and regression checks that gate each release.
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Serve and roll out in stages
We set up serving and a model gateway, with staged rollout and rollback, so a bad change can be reversed.
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Monitor and improve
We monitor drift, quality, latency and cost, and review the results to decide what to change next.
05 Capabilities and stack
Which capabilities and technologies are involved?
Capabilities
- MLflow
- Kubeflow
- Kubernetes
- Docker
- PyTorch
- Hugging Face
- Amazon SageMaker
- Google Vertex AI
The final stack follows your requirements and what you already run. How we choose technology
06 A typical situation
What does this usually look like?
A team has an AI assistant in production and wants to change a prompt or switch model provider, but has no way to tell whether the change makes answers better or worse.
We build an evaluation set from representative questions, add regression checks and a staged release with rollback, and monitor quality, latency and cost afterward.
07 Questions
Questions people ask
MLOps covers machine learning models, with training pipelines, registries, serving and drift monitoring. LLMOps covers applications built on foundation models, with prompts, retrieval, gateways, evaluation and token cost. A system can need both.
The controls should be in proportion to the risk. A small feature may need only versioned prompts, an evaluation set and basic monitoring, and we scale up from there when the stakes justify it.
We run comparisons on your own cases rather than on generic benchmarks, against the quality and speed your tasks need.
09 Terms