End-to-End MLOps Platform on Kubernetes

Overview

MLOps applies software engineering discipline to machine learning: versioned data and models, reproducible pipelines, automated deployment, and continuous monitoring. This Kubernetes-based platform is cloud-portable, so the same stack runs on any managed Kubernetes service or on-prem. Use it when multiple data science teams need a shared, governed path from experiment to production without each team building its own tooling.

Components

• Kubeflow Pipelines / Argo Workflows: define and run training pipelines as directed graphs of containerized steps.
• MLflow: tracks experiments, parameters, and metrics, and acts as the model registry with stage promotion.
• Feast: a feature store that serves consistent features to both training and serving.
• KServe: deploys registered models as autoscaling inference services with canary support.
• MinIO: S3-compatible object storage for datasets, artifacts, and model binaries.
• Prometheus: collects training and serving metrics, including prediction drift signals.

Data Flow

A pipeline run pulls a versioned dataset from MinIO, materializes features via Feast, trains a model, and logs metrics and the artifact to MLflow. If the model beats the current champion on holdout metrics, it is registered and promoted. A GitOps deployment then rolls it out through KServe as a canary, splitting a fraction of traffic to the new version. Serving metrics and live feature statistics flow to Prometheus, where drift or quality regressions trigger retraining.

Scaling and Resilience

Training steps request GPUs through Kubernetes node pools and scale out with parallel pipeline branches. KServe scales inference pods on request load and supports scale-to-zero for rarely used models. Argo retries failed steps and resumes pipelines from checkpoints. Keep artifacts and registry state in durable storage so the platform can be rebuilt declaratively. Run components across zones and back up MLflow's database.

Security

Manage all deployments through Git with reviews, giving an auditable history of what model ran when. Apply pod security standards and network policies to isolate tenants. Use least-privilege service accounts and a secrets manager for credentials. Sign model artifacts and verify them before deployment to prevent tampering. Record dataset and model lineage so any prediction can be traced to its training inputs.

Trade-offs and Alternatives

A self-managed Kubernetes platform avoids cloud lock-in and unifies tooling, but it carries real operational cost; managed offerings like SageMaker, Vertex AI, or Azure ML reduce that burden at the price of portability. Mixing many open-source projects creates integration overhead, so adopt incrementally and standardize interfaces. For small teams, a managed platform is usually the better first step; graduate to a custom Kubernetes platform only when scale and portability demand it.