T E K D E X

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TekDex is a technology-driven consulting and engineering company. We build agentic AI, cloud-native platforms and ServiceNow solutions for enterprises worldwide.

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MLOps & Model Ops

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Model Operations

MLOps & Model Ops

A model that works on the day it ships is not the achievement. The achievement is a model you can retrain in six months and get the same result, diagnose when it drifts, and roll back in minutes when it does not.

We build the operational layer around your models: versioned datasets, reproducible training runs, evaluation gates in the pipeline, monitoring that watches inputs as well as outputs, and a rollback that is a routine action rather than an emergency.

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What You Get

The operational floor we put under every model.

  • Versioned data and reproducible training — same inputs, same model
  • Evaluation gates in the pipeline that block a bad release
  • Drift monitoring on inputs and outputs, with rollback in minutes

Most teams discover they need this the first time a model quietly degrades and nobody can say when it started or what changed. Building it up front costs a fraction of reconstructing it under pressure.

Technical Questions, Answered

The data, almost always. An upstream schema change, a silently dropped column, a shifted distribution. The model itself is usually fine — which is why monitoring inputs matters at least as much as monitoring accuracy.
Minutes, because previous model versions stay deployable and traffic can be shifted back without a rebuild. If rollback needs a retrain, it is not a rollback.
Yes. We fit around what you already run — SageMaker, Vertex, Databricks, MLflow, Kubeflow or a pile of scripts and cron. Replacing a working stack is rarely the highest-value first move.
Input distributions moving away from training data, prediction distributions shifting, and feature-level nulls or ranges going out of bounds. You get an alert with the offending feature named, not a vague accuracy dip.