End-to-end ML fine-tuning pipeline on GCP with Vertex AI, Dataproc preprocessing, distributed training, and model registry.
Difficulty: advanced
Tags: ai, fine-tuning, training, ml-pipeline, gcp
Fine-tuning adapts pre-trained models to specific domains using curated datasets. This GCP-native pipeline covers the full lifecycle: data collection and cleaning via Dataproc, format conversion (JSONL, Parquet), distributed training across GKE GPU node pools, evaluation against held-out test sets, A/B comparison with baseline models, and promotion to the Vertex AI model registry. Designed for ML teams adapting foundation models to domain-specific tasks with reproducible experiments and version-controlled datasets.