MLOps and LLMOps
Production-grade ML and LLM operations — CI/CD pipelines, model monitoring, drift detection, cost governance, and continuous improvement frameworks.
Building a model is only half the challenge — keeping it performant, reliable, and cost-effective in production over time is the other half. Organizations that don't invest in MLOps infrastructure find themselves unable to iterate on models, unable to diagnose production failures, and paying for inference they can't control.
Techonroute helps organizations build the operational infrastructure to run ML and LLM systems sustainably. We design and implement CI/CD pipelines for model deployment, experiment tracking and model registries, monitoring systems that detect performance degradation and data drift, and cost governance frameworks for inference workloads.
LLMOps introduces unique challenges beyond classical MLOps: prompt versioning, evaluation pipeline design, managing context windows, and monitoring language model outputs for quality and safety. We address both the classical and LLM-specific dimensions of ML operations for production AI teams.
Services and Deliverables
CI/CD for ML Pipelines
Automated pipelines for training, testing, validating, and deploying model updates safely and repeatably.
Model Registry & Versioning
Centralized model registry with versioning, lineage tracking, and deployment approvals.
Experiment Tracking
MLflow, Weights & Biases, or custom experiment tracking for systematic model development.
Model Monitoring & Drift Detection
Production monitoring for data drift, prediction drift, and model degradation with alerting.
LLMOps Pipeline Setup
Prompt versioning, evaluation pipelines, and production quality monitoring for LLM-based systems.
Evaluation Pipeline Design
Automated evaluation frameworks that run on every model candidate before promotion to production.
Cost Governance for AI
Dashboards and controls for inference cost tracking, optimization, and budget enforcement.
Cloud AI Deployment
Deploy model serving infrastructure on AWS, GCP, or Azure with autoscaling and observability.
Is This Service Right for You?
You've built models but lack the operational infrastructure to deploy, monitor, and iterate on them reliably.
Your AI pilot worked — now you need the operational scaffolding to take it to production and keep it there.
You have several models in production and need a unified operational framework to manage them.
How an Engagement Works
Infrastructure Audit
Assess your current tooling, deployment processes, and monitoring gaps.
Architecture Design
Design the MLOps/LLMOps stack appropriate for your scale, tech stack, and team capabilities.
Pipeline Implementation
Build CI/CD, experiment tracking, model registry, and monitoring components.
Monitoring & Alerting Setup
Configure production monitoring with meaningful alerts and dashboards.
Team Enablement
Train your team on the tooling and processes so they can operate and extend the infrastructure.
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Ready to Build Your MLOps Infrastructure?
Move from fragile one-off deployments to reliable, observable production AI.