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MLOps Pipeline

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MLOps Pipeline Development - Ainexo

Who actually needs this

MLOps work makes sense once you have a trained model that works in a notebook and now needs to run reliably in production - serving real predictions, on a schedule or on-demand, without someone manually re-running a script. It does not make sense as a first step before you have a working model at all; we'll say so directly if a project describes itself as "MLOps" but is actually still at the model-development stage.

What we set up

  • Model serving - wrapping your trained model in an API endpoint your application can actually call, with proper request/response handling and error cases
  • Versioning - tracking which model version is live, so you can roll back if a retrained version underperforms
  • Monitoring - basic drift/performance tracking so you know when real-world data has started diverging from what the model was trained on, instead of finding out from a customer complaint
  • Retraining triggers - scheduled or threshold-based retraining pipelines, scoped to your actual data volume and update frequency, not a generic "daily retrain" that doesn't match your data reality

Stack

Python for the ML pipeline itself, with the serving layer built into your existing Node/MERN backend where possible rather than standing up an entirely separate system to maintain. Cloud deployment on standard providers (AWS, GCP, Azure) based on where your existing infrastructure already lives.

Pricing

Rs 150,000-600,000 depending on model complexity, whether retraining automation is in scope, and how much existing infrastructure we're integrating with versus building fresh.

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Frequently asked questions

Do you train the model, or just deploy an existing one?
This service is specifically for deploying and operating a model you already have. Model development itself is scoped separately under our AI development work.
What happens if the model's accuracy drops in production?
That's exactly what monitoring is for - we set up drift/performance tracking so degraded accuracy is visible before it becomes a customer-facing problem, not after.
Do we need a dedicated ML infrastructure team to maintain this?
No - we scope the pipeline to integrate with your existing backend so your regular development team can maintain it, rather than requiring specialized MLOps staff you don't have.
Which cloud provider do you use?
Whichever your existing infrastructure already runs on - AWS, GCP or Azure. We don't force a migration just to use our preferred stack.
How long does setup take?
Typically 6-12 weeks depending on whether retraining automation and drift monitoring are both in scope.
What does it cost?
Rs 150,000-600,000 depending on complexity and how much existing infrastructure is reused.
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