MLOps Consulting · San Francisco, California
MLOps consulting that makes models operate like software.
Squidcode builds the production ML infrastructure that turns trained models into reliable services — pipelines, serving, monitoring, and CI/CD for machine learning, run by the engineers who operate it.
What it means
The 90% that happens after training.
A trained model is the easy part. MLOps is everything that keeps it working in production: reproducible training pipelines, low-latency serving, monitoring that catches drift before users do, and CI/CD so a model update ships as safely as a code change.
We run this for our own products — including log.fail (AI log monitoring with automated incident summaries) and CodeMouse — so we are not theorising. We build the pipeline, wire up the observability, and leave you with something your team can operate.
Already have an ML stack that is fragile or manual? We also come in to harden existing pipelines and add the AI consulting glue that turns scripts into a platform.
Scope
What MLOps with us covers.
Training Pipelines
Reproducible, automated training and retraining pipelines with versioned data and models.
Model Serving
Low-latency, scalable serving — real-time or batch — built for your traffic and cost envelope.
Monitoring & Drift
Production monitoring for performance, data drift, and concept drift, with alerts that mean something.
CI/CD for ML
Automated evaluation gates and deployment so model changes ship like software, not science projects.
Feature & Data Pipelines
Reliable data and feature pipelines feeding training and inference from the same source of truth.
Cost & Scaling
Right-sized infrastructure and autoscaling so you pay for the inference you actually use.
How we work
Three steps, no theatre.
Call
A short scoping call. You describe the problem and constraints; we tell you honestly whether and how AI helps — and whether we are the right team.
Scope
A concrete plan: what we build, how we measure it, the timeline, and the path to production. No open-ended retainers dressed up as strategy.
Ship
We build, evaluate, and deploy — then hand over a running system with the monitoring and docs to operate it. We can keep running it if you want us to.
FAQ
Questions, answered.
What is MLOps consulting?
MLOps consulting is help designing and operating the infrastructure around machine learning models — training pipelines, serving, monitoring, drift detection, and CI/CD — so models run reliably in production instead of living in notebooks.
Do you set up model monitoring and drift detection?
Yes. We instrument production models for performance, data drift, and concept drift, and wire up alerting so issues surface before they reach users.
Which clouds and ML stacks do you work with?
We are stack-agnostic and work across the major clouds and common ML tooling. We pick what fits your team rather than forcing a particular vendor.
Can you fix or harden an existing ML pipeline?
Yes. A common engagement is taking a fragile, manual pipeline and turning it into a reproducible, monitored, CI/CD-driven platform.
How do we get started?
Email info@squidcode.com with a short description of your models and current setup. We will reply with next steps and set up a scoping call.
More services
Related AI consulting services.
Ready to operationalise your models?
Tell us what you are building. You will hear back from an engineer, not a funnel.
info@squidcode.com