MLOps Pipeline Setup
Most ML teams cobble together scripts and hope for the best. We build proper MLOps pipelines — automated training, validation gates, model registry, and deployment — so your models go from notebook to production without the duct tape.
Need this done for your project?
We implement, you ship. Async, documented, done in days.
Pipeline Architecture
We design DAG-based pipelines using tools like Kubeflow Pipelines, Airflow, or Prefect — depending on your existing stack. Each step (data extraction, preprocessing, training, evaluation, registration) runs in its own container with pinned dependencies. No more 'works on my laptop' issues when training runs hit production data.
Automated Training & Validation
Training jobs trigger on schedule or on data drift detection. Every run logs hyperparameters, metrics, and artifacts to MLflow or Weights & Biases. Validation gates check accuracy thresholds, data distribution shifts, and inference latency before any model gets promoted. Failed runs alert your team — they never silently deploy.
Model Registry & Versioning
Every model gets versioned with its training data hash, code commit, and hyperparameters. The registry tracks staging vs. production promotions. Rollback is a single command — point the serving endpoint back to the previous version. No ambiguity about what's running where.
What You Get
A fully automated pipeline that trains, validates, registers, and deploys models without manual intervention. CI/CD for ML that actually works. Documentation covering pipeline architecture, runbooks for common failure modes, and Grafana dashboards tracking model performance over time.
Why Anubiz Engineering
Ready to get started?
Skip the research. Tell us what you need, and we'll scope it, implement it, and hand it back — fully documented and production-ready.