GenAI Use Cases | Kubeflow

Kubeflow subprojects are powering every stage of the GenAI application lifecycle
From generating synthetic data to retrieval-augmented generation (RAG), fine-tuning large language models (LLMs), hyperparameter optimization, inference at scale, and evaluation, Kubeflow’s modular, Kubernetes-native architecture makes building end-to-end GenAI pipelines both reproducible and production-ready.
When real data is scarce or sensitive, Kubeflow Pipelines automates the creation of high-fidelity synthetic datasets using techniques like GANs, VAEs, and statistical copulas. By defining parameterized pipeline components that:
teams can accelerate experimentation without compromising compliance. Learn more in the Synthetic Data Generation with KFP guide.
RAG combines vector-based retrieval with generative models to produce contextually grounded outputs:
This workflow is detailed in the Katib RAG blog post and the Feast + Docling RAG tutorial.
Preprocessing massive document collections for RAG pipelines—text cleaning, chunking, and embedding generation—can become a bottleneck. Kubeflow integrates with the Spark Operator to run distributed Spark jobs on Kubernetes, allowing you to:
Kubeflow Trainer provides a seamless way to fine-tune large language models (LLMs) at scale using popular frameworks such as PyTorch, DeepSpeed, MLX, and others.
With the Kubeflow SDK and the train() API, you can define custom fine-tuning scripts and scale them across thousands of GPUs. For detailed usage, see the Kubeflow Trainer documentation:
You can implement a wide range of advanced algorithms to fine-tune your LLMs such as supervised fine tuning (SFT), knowledge distillation, Direct Preference Optimization (DPO), Proximal Policy Optimization (PPO), Group Relative Policy Optimization (GRPO), quantization-aware training, and more.
Kubeflow Trainer enables efficient distributed training with both data parallelism and model parallelism, ensuring optimal GPU utilization and reduced training time.
For a faster start, you can use the BuiltinTrainers(), which provide pre-configured blueprints for LLMs fine-tuning. Simply specify the desired configuration to get started:
For details, see the Kubeflow Trainer TorchTune guide.
Automated tuning is essential to maximize model performance:
Katib Experiments let you define hyperparameter search spaces and optimization objectives (e.g., minimize loss, maximize BLEU).
Katib allows you to effortlessly optimize hyperparameters of LLMs using distributed PyTorchJobs.
After training and tuning, KServe delivers scalable, framework-agnostic inference:
Expose models via Kubernetes Custom Resources
Automate blue/green or canary rollouts
Autoscale based on real-time metrics (CPU, GPU, request latency)
Integrate KServe endpoints into Pipelines to orchestrate rollout strategies and ensure consistent low-latency GenAI services.
Rigorous evaluation guards against drift and degradation:
Pipeline-Embedded Eval Steps
Early-Stopping & Tuning
By codifying evaluation in Pipelines, you maintain reproducibility and versioned lineage from data to model to metrics.
Kubeflow’s ecosystem continues to expand:
Notebooks UI for interactive development (Jupyter, VSCode)
Model Registry for artifact versioning & lineage
Spark & Batch Operators for data-intensive preprocessing
Feast supports vector similarity search for RAG and other next-gen AI workloads
Each Kubeflow Project plugs seamlessly into Kubernetes, ensuring portability and consistency.
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