MLOps & Model Deployment
I get machine learning models off laptops and into production. Model serving, GPU autoscaling, CI/CD for models, monitoring, and rollback, so your team ships models reliably and watches them in production instead of guessing.
Sound familiar?
- ✕A model works in a notebook but there is no clean path to production
- ✕Deployments are manual, scary, and nobody wants to touch the serving stack
- ✕GPU costs are high because serving never scales down
- ✕You ship a new model version and have no way to roll back fast
- ✕Model quality drifts in production and you find out from users, not metrics
- ✕Inference latency spikes under load and you cannot tell why
What you get
- ✓A repeatable deploy pipeline that ships models the same way every time
- ✓Model serving with GPU autoscaling that scales to zero when idle
- ✓Canary or shadow rollouts so a bad model never hits all traffic
- ✓Monitoring for latency, errors, cost, and drift, with alerts that mean something
- ✓One-command rollback when a release goes wrong
- ✓A serving stack your team can run after I leave
Getting a model to production is an infrastructure problem
A model that scores well in a notebook is the start, not the finish. The hard part is everything around it. A serving layer that holds up under load, a pipeline that deploys the same way every time, versioning so you can undo a bad release, and monitoring that tells you when quality slips before your users do. That gap is where most ML projects stall, and it is the gap I close.
What I help with
- Model serving. Real-time or batch serving for classic ML and LLMs, on Kubernetes or a managed platform, sized to the model.
- GPU autoscaling. Scale serving with traffic and down to zero when idle, so you stop paying for idle accelerators.
- CI/CD for models. A pipeline that packages, tests, and ships a model the same way every time, with no manual steps to forget.
- Safe rollouts. Canary and shadow deploys, version pinning, and one-command rollback so a bad model never takes the whole service down.
- Monitoring and drift. Latency, error, cost, and quality metrics with alerts that fire on real problems, not noise.
- AIOps. Anomaly detection and automation that cuts alert noise and catches issues early.
Why me for this
Model deployment and serving infrastructure is my day job at TrueFoundry, where I work on the platform that other teams use to ship models. You get someone who lives in GPU scheduling, autoscaling, and serving at production scale, and who also knows the Kubernetes and networking underneath it. The model layer and the cluster, from one person.
How an engagement works
- Scope. What you are deploying, your latency, cost, and reliability bar, and the realistic path to it.
- Architect. The simplest serving and pipeline design that clears the bar.
- Build and measure. Ship it with monitoring and safe rollouts, so a release is boring instead of scary.
- Hand off. Pipelines, runbooks, and a serving stack your team can run without me.
Frequently asked questions
What does MLOps actually cover?+
The work between a trained model and a reliable production service. Packaging the model, a serving layer that scales, a CI/CD pipeline so deploys are repeatable, versioning so you can roll back, and monitoring for latency, cost, and quality drift. The training is yours. I own the path from "it works on my machine" to "users depend on it."
Can you deploy both classic ML models and LLMs?+
Yes. The shape is the same. Package, serve, scale, monitor, roll back safely. LLMs add GPU scheduling, batching, KV-cache tuning, and a gateway for routing and cost control. Classic models lean more on feature pipelines and batch versus real-time serving. I have shipped both, and model-serving infrastructure is my day job at TrueFoundry.
How do you keep GPU serving costs sane?+
Most GPU bills are high because serving runs at a fixed size around the clock. I set up autoscaling that scales with real traffic and to zero when idle, batch requests where latency allows, right-size the instance to the model, and put a gateway in front to route cheaper requests to cheaper models. Teams usually see the bill drop on the next cycle.
How do you deploy a new model version without breaking production?+
New versions go out as a canary or shadow first, so a fraction of traffic or a mirror of traffic hits the new model while the old one still serves. Metrics decide whether it gets promoted. If something looks wrong, rollback is one command. Nothing goes out as a big-bang swap with the whole service riding on an untested release.
What is AIOps and do you do it?+
AIOps is using data and ML to run operations. Anomaly detection on metrics, smarter alerting, and automating the tedious parts of incident response. I do the practical version, which is wiring up monitoring and automation that cuts noise and catches problems early, rather than selling a magic dashboard. It pairs naturally with the deployment and observability work.