MLOps Consulting & Implementation
InterCode builds the ML infrastructure that takes models from research notebooks to production systems. We design training pipelines, model registries, serving infrastructure, and automated retraining loops so your machine learning investments generate reliable business value at scale.
Turning Experimental ML Into Production Systems
Most machine learning projects stall between a working notebook and a production system. MLOps — the practice of applying DevOps principles to the ML lifecycle — closes that gap. It encompasses everything from feature engineering and experiment tracking through model deployment, monitoring, and automated retraining. Without MLOps, models degrade silently as data distributions shift and teams cannot reproduce past results. At InterCode, we implement end-to-end MLOps pipelines tailored to your stack and team size. We set up feature stores using Feast or Tecton to ensure training and serving features are consistent. We configure experiment tracking and model registries with MLflow or Weights & Biases so every model version is reproducible and auditable. Training pipelines are codified in Kubeflow Pipelines, Apache Airflow, or SageMaker Pipelines and triggered on schedule or by data events. For model serving, we deploy to Triton Inference Server, BentoML, or SageMaker Endpoints with A/B testing and canary rollouts. We instrument production models with concept drift detectors — statistical tests that alert when incoming data diverges from training distributions — and wire them to automated retraining triggers. GPU infrastructure is managed via Kubernetes with autoscaling node pools or SageMaker managed training, depending on your cloud environment.
What We Build With MLOps
We build CI/CD pipelines for ML models where a pull request to a model training repo triggers automated training, evaluation, and conditional promotion to production — treating models as code. We set up automated retraining workflows that detect feature drift or model performance degradation and trigger a new training run without human intervention. For teams still running ad-hoc notebooks, we migrate their experimentation workflows to structured pipelines with versioned datasets, reproducible environments, and centralized experiment tracking. We build feature stores for recommendation systems that serve pre-computed features at low latency. For enterprises managing many models, we design multi-model serving platforms with unified monitoring dashboards and rollback capabilities.
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Learn moreFrequently Asked Questions
MLOps is the set of practices and tools that make machine learning models reliable in production. It covers the full lifecycle: data pipelines, feature engineering, experiment tracking, model training, deployment, monitoring, and retraining. Without MLOps, models degrade over time as data changes, teams cannot reproduce results, and there is no safe way to update a model in production.
DevOps manages the deployment and reliability of software applications. MLOps extends those principles to machine learning, where the deployable artifact is not just code but a trained model that depends on data. MLOps adds concerns like experiment tracking, model versioning, data drift monitoring, and triggered retraining that do not exist in traditional software deployment.
We choose tools based on your infrastructure. For AWS-native teams, SageMaker Pipelines and the SageMaker Model Registry are the lowest-friction path. For GCP teams, Vertex AI Pipelines fits naturally. For multi-cloud or on-premise environments, we use Kubeflow for orchestration and MLflow for experiment tracking and the model registry. Weights & Biases is our preferred choice when rich experiment visualisation and collaboration features matter.
An initial MLOps setup for a single model — CI/CD pipeline, model registry, basic monitoring, and a deployment endpoint — typically takes 4-8 weeks and costs $20,000–$60,000 depending on complexity. A full enterprise MLOps platform with feature store, multi-model serving, and automated retraining is a 3-6 month engagement. We scope each project individually after reviewing your current infrastructure and model portfolio.
A basic MLOps setup for one model team — pipeline automation, experiment tracking, and a staging-to-production promotion workflow — takes 4-6 weeks. A full platform with feature store, drift monitoring, and automated retraining across multiple models takes 3-6 months. We recommend starting with a single high-value model to demonstrate ROI before scaling the platform.
MLOps scales to team size. A two-person team benefits from lightweight tooling: MLflow for experiment tracking, GitHub Actions for model CI/CD, and a single SageMaker endpoint. The overhead is low and the gains — reproducibility, safe deployments, and drift alerts — are immediate. Enterprise MLOps adds feature stores, multi-model governance, and platform engineering that only make sense at scale.
Build Your MLOps Foundation
Talk to our ML engineers about building production ML infrastructure. We will assess your current setup and design a pragmatic MLOps roadmap that fits your team and stack.
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