GenAI Use Cases

Generative AI (GenAI) workflows typically span multiple stages—from data preparation to model fine-tuning, prompt engineering, evaluation, and deployment. Kubeflow Pipelines provides a flexible and scalable orchestration engine to support these end-to-end workflows in a reproducible, modular way.

Data Preparation

Effective GenAI starts with high-quality, well-structured data. Use Kubeflow Pipelines to:

  • Ingest and preprocess unstructured data such as PDFs, HTML, images, or audio.

  • Convert raw documents into structured formats and chunk them for tokenization.

  • Clean, normalize, and deduplicate datasets for training and evaluation.

  • Generate embeddings using models like SentenceTransformers or CLIP.

  • Create and store metadata-rich artifacts for traceability and downstream reuse.

Fine-tuning & Training

Once data is prepared, Kubeflow Pipelines can orchestrate training jobs at scale:

  • Automate tokenization and model fine-tuning (e.g., LoRA, full fine-tuning).

  • Parallelize hyperparameter sweeps (e.g., learning rate, batch size) using conditional and parallel components.

  • Leverage GPUs, TPUs, or managed training backends across environments.

  • Use pipeline components to separate data prep, training, and checkpoint saving.

Prompt Engineering Experiments

Experiment with prompt templates using parameterized pipelines:

  • Evaluate prompt effectiveness at scale using batch scoring jobs.

  • Log and compare model outputs with evaluation metrics and annotations.

  • Enable iterative prompt design with easy-to-swap text templates.

Evaluation & Monitoring

Build pipelines to evaluate and monitor model outputs:

  • Compare generations against reference outputs using BLEU, ROUGE, or custom metrics.

  • Integrate human-in-the-loop review and scoring.

  • Run periodic evaluation pipelines to detect degradation or drift in output quality.

Inference & Deployment

Turn generative models into production services with reproducible deployment steps:

  • Package and deploy models as containerized services using KServe or custom backends.

  • Use CI/CD pipelines to roll out new versions with A/B testing or canary releases.

  • Scale endpoints dynamically based on request volume and latency metrics.

Multimodal Generative Workflows

Design rich pipelines that support multiple input/output modalities:

  • Combine text, image, and audio generation into a unified DAG.

  • Orchestrate complex workflows involving model chaining and data routing.

  • Use custom components to process modality-specific inputs and outputs.

See Also