Workflow:Kubeflow Kubeflow AI Lifecycle Pipeline
| Knowledge Sources | |
|---|---|
| Domains | MLOps, LLMs, Model_Training, Model_Serving |
| Last Updated | 2026-02-13 14:00 GMT |
Overview
End-to-end AI/ML lifecycle covering the build-train-deploy journey using Kubeflow's composable sub-projects on Kubernetes.
Description
This workflow describes the complete AI lifecycle as orchestrated by the Kubeflow platform. It maps the journey from data preparation and experimentation through distributed model training, hyperparameter optimization, model registration, and production serving. Each stage is handled by a dedicated Kubeflow sub-project: Notebooks for experimentation, Pipelines for workflow orchestration, Trainer for distributed training, Katib for hyperparameter tuning, Model Registry for versioning, and KServe for inference serving. The workflow demonstrates how these independently usable components integrate into a cohesive AI platform.
Usage
Execute this workflow when you have a deployed Kubeflow platform and need to take an AI/ML project from initial experimentation to production serving. This is the core "Golden Path" for data scientists and ML engineers who want to leverage Kubeflow's full capabilities for a reproducible, scalable AI workflow.
Execution Steps
Step 1: Experiment_and_Prototype
Use Kubeflow Notebooks (Workbenches) to create an interactive development environment for data exploration and model prototyping. Notebooks provide Jupyter, RStudio, and VS Code environments with direct access to cluster resources, GPUs, and shared storage volumes.
Key considerations:
- Select the appropriate container image with required ML frameworks
- Configure GPU resources if needed for initial prototyping
- Use shared PersistentVolumes for data that persists across notebook restarts
- Notebooks run in user-isolated namespaces with RBAC controls
Step 2: Build_Pipeline
Define the ML workflow as a Kubeflow Pipeline using the KFP SDK. The pipeline encodes the sequence of steps (data preprocessing, training, evaluation, deployment) as a directed acyclic graph (DAG) of containerized components. Each component has defined inputs, outputs, and resource requirements.
Key considerations:
- Use the KFP v2 SDK for pipeline definition
- Each pipeline step runs as an isolated container with explicit dependencies
- Pipeline artifacts (datasets, models, metrics) are tracked automatically via MLMD
- Pipelines are versioned and can be shared across teams
Step 3: Train_Model
Submit distributed training jobs using Kubeflow Trainer. Trainer supports multiple frameworks (PyTorch, TensorFlow, JAX, MPI) and handles the orchestration of multi-node, multi-GPU training. It manages worker pod lifecycle, fault tolerance, and elastic scaling.
Key considerations:
- Trainer V2 provides a unified API across all supported frameworks
- Configure gang scheduling (e.g., Volcano) for efficient GPU allocation
- Elastic training allows recovery from spot instance preemption
- Training metrics and logs are captured for debugging and monitoring
Step 4: Tune_Hyperparameters
Use Katib to automatically search for optimal hyperparameters. Katib runs multiple training trials with different parameter configurations using algorithms like Bayesian optimization, random search, or population-based training. Early stopping reduces wasted compute on underperforming configurations.
Key considerations:
- Define the search space (learning rate, batch size, model architecture choices)
- Select an optimization algorithm appropriate for the search space
- Configure early stopping to save compute on poor-performing trials
- Katib integrates with Trainer for each trial execution
Step 5: Register_Model
Store the trained model artifact in the Kubeflow Model Registry. The registry tracks model versions, metadata (training parameters, metrics, lineage), and deployment state. It provides a catalog for discovering and governing models across the organization.
Key considerations:
- Record training provenance (dataset version, hyperparameters, metrics)
- Tag models with deployment readiness status
- Model Registry integrates with KServe for deployment triggers
- Support for async upload and reconciliation with serving infrastructure
Step 6: Serve_Model
Deploy the registered model to production using KServe. KServe provides serverless inference with autoscaling, canary rollouts, request batching, and multi-model serving. It supports all major serving runtimes (TensorFlow Serving, TorchServe, Triton, custom containers).
Key considerations:
- KServe supports both serverless (Knative) and raw Kubernetes deployment modes
- Canary rollouts enable gradual traffic shifting to new model versions
- Autoscaling (including scale-to-zero) optimizes resource utilization
- Transformers handle pre/post-processing as sidecars
- Explainability integrations (Alibi) provide model interpretability
Step 7: Monitor_and_Iterate
Monitor model performance in production and feed insights back into the development cycle. Use pipeline metadata tracking to compare model versions, detect drift, and trigger retraining when performance degrades.
Key considerations:
- Track inference latency, throughput, and error rates
- Compare production metrics against training evaluation metrics
- Set up automated retraining pipelines triggered by performance thresholds
- Use the Central Dashboard for a unified view across all lifecycle stages