Kubeflow

Monitor data drift using KServe metrics.

Trigger a Kubeflow Pipeline via a webhook when drift exceeds a threshold.

The pipeline pulls new data, retrains the model, and validates performance.

Automated deployment to staging if validation passes.

Define a PyTorchJob custom resource in Kubeflow.

Specify the number of worker nodes and GPU resources per node.

Kubeflow Training Operator orchestrates the distributed setup.

Model weights are synchronized across nodes via NCCL.

Submit a Katib Experiment defining the search space for parameters.

Katib launches parallel training trials within the cluster.

The system identifies the best-performing trial based on objective metrics.

The best parameters are automatically passed to the final training pipeline.

Set up identical Kubeflow instances on-prem and in AWS.

Train models on-prem using local H100 clusters.

Package the model into a standard KServe container.

Deploy the container to the public cloud Kubeflow instance for low-latency serving.

Admin sets resource quotas for each team member in the Kubeflow Dashboard.

Users spawn custom Notebook servers with specific GPU/RAM requests.

Environment snapshots allow sharing exact workspaces between team members.

Integrated storage volumes persist work across restarts.

Use KServe to deploy 'v2' of a model alongside 'v1'.

Configure the InferenceService to route 10% of traffic to v2.

Monitor performance metrics in real-time.

Gradually increase traffic to 100% if the model remains stable.

Query the Kubeflow Metadata store for a specific model version.

Retrieve the exact pipeline run ID that produced the model.

Inspect the dataset hash and code commit recorded in the metadata.

Generate a lineage report for the compliance officer.