End-to-end ML pipelines — experiment tracking, model registry, feature stores, CI/CD for models, drift detection, and rollback. Built on Kubernetes.
Talk to an MLOps engineerMost ML projects die in the gap between notebook and production. A model that works in a Jupyter notebook is not a product — you need experiment tracking, model registry, feature stores, CI/CD for models, drift detection, and rollback. Without these, you can't reproduce results, can't detect when a model goes stale, and can't roll back when a deployment breaks.
CB4UHost builds MLOps platforms on Kubernetes using open-source tools — MLflow or Weights & Biases for tracking, KServe or Seldon for serving, Feast for feature stores, Evidently for drift detection. We don't lock you into proprietary platforms; you own the stack.
We're cost-aware from day one. Spot instances for batch training, dynamic batching for inference, KV-cache tuning for LLMs, and autoscaling that actually scales down (not just up). Most teams overspend on ML infrastructure by 30-50% — we close that gap.
Every engagement ends with runbooks, dashboards, and training so your team can operate the platform after we leave. No black boxes, no 'consultant-only' workflows.
MLflow or W&B integration — every training run logs params, metrics, artifacts, and code version for reproducibility.
Versioned model registry with stage transitions (staging → production → archived) and approval workflows.
Feast or custom feature store so training and serving use the same features — no training/serving skew.
GitHub Actions / GitLab CI pipelines that retrain, evaluate, and deploy models on data or code changes.
Evidently or custom monitors that detect data drift, concept drift, and prediction drift — with alerting.
One-command rollback to previous model version, plus dashboards for latency, throughput, error rate, and drift.
We review your current ML workflow, identify gaps, and recommend an MLOps stack that fits your team and budget.
We stand up the MLOps platform on Kubernetes with IaC — reproducible, version-controlled, teardown-friendly.
We wire your existing training code into the platform — tracking, registry, feature store, CI/CD.
We add drift detection, alerting, and dashboards so you know when models degrade.
We hand over runbooks, dashboards, and a 2-day training session so your team owns the platform.
Interactive tools related to this service.
MLflow is open-source and self-hosted (free, but you operate it). W&B is managed (paid, but excellent UX). We recommend MLflow for teams that want to own their stack, W&B for teams that want to move fast and don't mind the per-user pricing.
For production MLOps with autoscaling, multi-model serving, and rollback — yes. For a single model with low traffic, you can run on a single VM with Docker Compose. We'll recommend based on your scale.
We deploy Evidently (or custom monitors) that track input data distribution, prediction distribution, and ground-truth (when available) over time. When drift exceeds a threshold, we alert and trigger a retraining pipeline.
Yes. We integrate model training, evaluation, and deployment into your existing CI/CD — no need to switch to a separate ML platform.
Compare H100, A100, L40S, and consumer GPU pricing across 8+ providers — benchmarked against your actual workload, not affiliate payouts.
Adapt open-source LLMs (Llama, Mistral, Qwen, DeepSeek) to your domain — with LoRA, QLoRA, instruction tuning, and safe deployment guardrails.
Production retrieval-augmented generation with vector stores, hybrid search, reranking, citation, and observability — so you can trust the answers.
Tell us about your project. We'll come back with a scoped proposal and a fixed-fee quote.
Talk to an MLOps engineer