MLOps, Scaling & AI Operations

Many teams can train a promising model in a notebook; far fewer can keep that model healthy in production for months or years. We step in to design and implement MLOps practices that match your context: versioned datasets and models, reproducible training pipelines, automated deployment and rollbacks, and clear ownership of each stage of the lifecycle.

Our typical stack includes tools like MLflow, Weights & Biases, DVC, Kubeflow or custom pipelines built on top of Airflow, Prefect or Dagster. Models are packaged into Docker images and served via REST/gRPC, Triton Inference Server, TorchServe or custom GPU/CPU microservices. We implement canary and blue-green deployments, A/B tests, and automated rollback strategies so you can ship improvements without fear of breaking production.

Operations don’t stop at deployment. We set up monitoring for data drift, model performance, latency and cost across your infrastructure. Logs and metrics flow into centralized observability stacks (Prometheus, Loki, Grafana, ELK/EFK), and we define alerting rules so your team knows when behavior changes. When needed, we build retraining and evaluation loops that regularly refresh models with new data, keeping your AI systems aligned with the real world instead of the dataset from six months ago.