How to Set Up MLOps for a Startup
The right maturity level, not over-engineered - the MLOps setup that grows with your team.
Most startups either have no MLOps (models deployed by copying pickle files) or try to implement enterprise MLOps too early (Kubeflow on Kubernetes for a team of three). This guide covers the pragmatic middle path: the MLOps setup that is right for a startup with 1-10 models in production, without the enterprise complexity you dont need yet.
No fluff. Production-grade answers from engineers who ship AI into real products.
What MLOps a Startup Actually Needs vs What Enterprises Do
Enterprise MLOps includes feature stores, model governance workflows, A/B testing infrastructure, and automated retraining pipelines. A startup with one or two models in production needs: experiment tracking so you can reproduce any result, a model registry so you know what is in production, and a deployment pipeline that is not 'copy the file to the server'. Start here. Add complexity when the pain demands it.
At Valletta.Software, we focus on:
Experiment tracking: MLflow self-hosted on a $20/month VPS - log every experiment from day one
Model registry: MLflow Model Registry - staging production archived - three states is enough
Versioning: DVC for dataset version control linked to model versions - reproducibility is not optional
Deployment: FastAPI serving container on ECS or Cloud Run - not flask dev server
CI for ML: retrain on data trigger evaluate promote or reject - not manual promotion
Monitoring: prediction distribution and business metric tracking - not just infrastructure metrics
On-call: who owns the model when it degrades at 3am - define this before it happens
The Startup MLOps Stack That Does Not Require a Dedicated MLOps Engineer
This is the setup that lets two data scientists ship models without a dedicated MLOps hire.
We give you more than just people. We give you top performers who drive results.
MLflow: self-hosted on a small VM - tracking server artifact store model registry in oneDVC: git for data and models - version datasets link to experiments reproduce any training runAirflow or Prefect: one retraining pipeline - triggered by schedule or data quality checkDocker: one Dockerfile per model serving container - same image dev staging prodGitHub Actions: build test push to registry deploy to staging - ML CI same as software CIEvidently: data drift report weekly - email alert if distribution shift detectedFastAPI: model serving with /predict /health /metrics endpoints - not Jupyter serving
Set up production infra - CI/CD, Docker, Kubernetes, monitoring - from day one
Ship 3x faster with AI-native tooling and vibe-to-production methodology
Deploy properly - not just Vercel free tier - with autoscaling and observability
Audit your vibe-coded codebase and remediate before production incidents
How to Set Up MLOps for a Startup - With Engineers Who Match Complexity to Stage
Lets keep it simple.
Our engineers use Cursor, Claude Code, and AI-native tooling daily - not just to build AI products, but to ship them to production, maintain them, and scale them.
Lets keep it simple.
Lets keep it simple.
Our MLOps engineers have set up pipelines for startups at seed stage through Series B. We know when Airflow is overkill (it usually is at seed) and when the simple cron job breaks. We start at the right level and add complexity only when the team needs it.
Ready to Ship Your Models Properly? Lets Set Up the Pipeline.
Our MLOps engineers set up experiment tracking, model registry, CI/CD for ML, and deployment - the right level of complexity for your team size and model count.
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