MLOps Services | Production ML Pipelines, Model Deployment & Monitoring
Stop shipping notebooks. Start shipping models.
ENQCODE designs and builds the operational foundation that turns ML and GenAI experiments into reliable production systems — training pipelines, feature stores, model registries, deployment, monitoring, and retraining. Built on AWS SageMaker, Vertex AI, Azure ML, MLflow, Kubeflow, and modern open-source tooling.
Reproducible, automated training pipelines on Airflow, Prefect, Kubeflow, or SageMaker Pipelines. Versioned data, versioned code, versioned environments. The same training run can be reproduced six months later, audited, and retrained on schedule.
Build a feature store (Feast, Tecton, Vertex Feature Store, or Databricks) so the same features are available consistently in training and serving — no more train/serve skew. Online and offline access, point-in-time correctness, and proper governance.
Version, stage, promote, and roll back models like you do code. MLflow, SageMaker Model Registry, or Vertex Model Registry — wired into CI/CD with automated eval gates. Bad models never reach production silently.
Real-time, batch, and streaming model serving — SageMaker endpoints, Vertex Online Prediction, BentoML, KServe, or custom FastAPI / vLLM / Triton. Auto-scaling, A/B and shadow traffic, canary rollouts, and proper rollback.
Production monitoring for input drift, output drift, latency, throughput, error rates, and the business KPI the model is supposed to move. Evidently, Arize, WhyLabs, or custom — with Slack/PagerDuty alerts so issues are caught in hours, not quarters.
The MLOps stack for LLMs: prompt versioning, eval harnesses, structured-output validation, cost dashboards, prompt-cache hit rates, hallucination monitoring, jailbreak detection, and golden-set regression on every prompt or model change.
Discovery
When you come to us with your idea, we start by analyzing your requirements, goals, and the feasibility of the project. This step helps both sides: you polish your idea and clarify demands while we understand your business and estimate the project’s timeline and costs.
Data Exploration
Our AI engineers run exploratory data analysis on your existing datasets and infrastructure to confirm there is enough signal for the project. We clean the data, identify gaps, and design how to use it as efficiently as possible before any model work begins.
Development
For larger projects we start with a small-scale prototype to validate that the final model will work in practice. Once the concept is proven we move to full-scale development in two-week sprints with daily updates and a weekly call to share progress.
Testing & QA
Our QA specialists run a wide range of tests — accuracy, latency, edge cases, prompt-injection and hallucination checks for LLM-based features — to make sure the final model behaves reliably. Anything that needs fixing gets resolved before deployment.
Deployment
We integrate the AI solution into your infrastructure with proper observability, cost monitoring, and guardrails. You start using it in production and can immediately measure the impact on the parts of your business it touches.
Monitoring & Support
AI models drift as data shifts, so deployment is not the end. ENQCODE provides ongoing monitoring for accuracy, cost, latency, and safety — plus retraining and updates as needed — so the system keeps performing long after launch.
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Committed to Excellence
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Quality solutions, on-time delivery, post-launch support.