MLOps and AIDeployment
Turn a promising notebook or demo into a service that deploys repeatably, is monitored for quality and cost, and can be rolled back when it misbehaves.
MLOps and AI Deployment: what the work involves
Many AI projects stall between the demo and the real product. The model lives in a notebook that only one person can run, dependencies are unpinned, and a new version means copying files by hand. Nobody knows which data trained which model, a prompt change goes live without testing, and the first sign of a quality problem is a customer complaint. Costs creep up with no per-feature view.
DevKey builds the delivery path around your model or LLM application. Code and prompts are versioned, experiments tracked, and packaging is containerised. A pipeline runs tests and evaluation sets on every change and blocks releases that regress. Serving is set up for your latency and scale needs, whether through managed endpoints or self-hosted inference. In production we add logging, cost and latency dashboards, drift and quality checks, alerts, and a documented rollback, then train your team to operate it.
Core features
Reproducible environments
Dependencies, model versions and configuration are pinned and containerised, so what ran in testing is what runs in production.
Experiment and model registry
Runs, datasets, metrics and artefacts are tracked, giving every deployed model a traceable record of how it was produced.
CI/CD with evaluation gates
Each change triggers tests and an evaluation suite, and the pipeline refuses to ship if quality, latency or cost cross agreed limits.
Serving and scaling
Models and LLMs are served through APIs with batching, caching and autoscaling, using GPU or CPU resources matched to the load.
Observability for AI
Traces, token usage, latency, error rates, user feedback and drift indicators appear on dashboards with alerts for regressions.
Rollback and release strategy
Canary or shadow releases and fast rollback mean a bad model version can be reverted without an outage.
What we get right before launch
Evaluation is the hard part
Without a trustworthy test set, automation only ships mistakes faster. We invest early in evaluation data and metrics that reflect your users, and keep human review samples running in production.
Cost of GPUs and tokens
Idle GPUs and chatty prompts are expensive. We measure cost per request, right-size instances, use smaller models where acceptable, and compare self-hosting with hosted APIs honestly, including the staff time to run it.
Lock-in and portability
Tying everything to one cloud service or model vendor makes later changes expensive. We prefer open formats and thin abstraction layers, and note where a managed service saves effort at the price of portability.
Tools and technology
- Docker
- Kubernetes
- MLflow
- vLLM
- FastAPI
- GitHub Actions
- Prometheus and Grafana
- Python
- AWS SageMaker and GCP Vertex AI
Common questions, answered
Do we need Kubernetes for this?
Not necessarily. Many AI features run well on a few containers or managed endpoints. We recommend the simplest platform that meets your scale and reliability needs, and move to Kubernetes only when there is a clear reason.
Can you deploy on our own servers?
Yes, including on-premises or private cloud with GPUs. Constraints such as air-gapped networks affect model choice and updates, so we assess the environment first and plan for how models and dependencies will be delivered.
How do you monitor LLM quality in production?
We combine automated evaluations on sampled traffic, user feedback signals and rule-based checks, with human review of a sample. None are perfect alone, so we use several and tune alerts to avoid noise.
Will you train our team to run it?
Yes. We document runbooks, set up dashboards with sensible alerts, and walk your engineers through releases and incidents, so you are not dependent on us for day-to-day operation.
More AI Agents & Automation services
All AI Agents & Automation servicesPrivate LLM Deployment
Keep prompts and documents inside your own environment by running an open model you control, with the sizing, security and quality evidence to justify the choice.
LLM Fine-Tuning Services
Find out whether fine-tuning will actually beat better prompting or retrieval for your task, and if it will, get a tuned model with the evidence to prove it.
AI Integration for Existing Software
Add AI to the product or internal system you already run, through clean service boundaries and a gradual rollout, with no rewrite and no big-bang release.
Ready to start your MLOps and AI Deployment project?
Tell us what you need and we will come back with a clear scope, timeline and the questions worth answering before any build starts.