🧠 AI & Machine Learning · Deployment · Pro
MLOps Model Training Pipeline
Where each part of an MLOps setup runs: feature store, training jobs on GPU nodes, experiment tracking, model registry and automated deployment to serving.
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Drawing diagram…
What this diagram shows
- Training runs on a separate GPU node pool
- Every model version is registered with its metrics
- Only approved models are promoted to production
Prompt used
Deployment of an MLOps platform on Google Cloud: data lands in BigQuery and a Feast feature store. Kubeflow Pipelines on GKE runs training jobs on a GPU node pool, logs runs to MLflow, and registers models in the MLflow model registry. After approval, Argo CD deploys the model to a KServe serving cluster behind a load balancer. Models and artifacts are stored in Cloud Storage.
Mermaid code
flowchart TB
DS[Data Scientists] --> KF
subgraph GCP[Google Cloud]
BQ[(BigQuery)]
FS[(Feast Feature Store)]
GCS[(Cloud Storage - artifacts)]
subgraph TRAIN[GKE Training Cluster]
KF[Kubeflow Pipelines]
subgraph GPU[GPU Node Pool]
JOB[Training Jobs]
end
MLF[MLflow Tracking and Registry]
end
subgraph SERVE[GKE Serving Cluster]
ARGO[Argo CD]
KS[KServe Model Pods]
end
LB[Load Balancer]
end
BQ --> FS
KF --> JOB
FS --> JOB
JOB --> MLF
JOB --> GCS
MLF -->|Approved version| ARGO
ARGO --> KS
GCS --> KS
LB --> KS
APPS[Client Apps] --> LBRelated templates
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