AI Infrastructure
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.
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Data preprocessing runs on Dataproc Spark clusters that scale based on dataset size. Training jobs use GKE with GPU node pools and support data parallelism across multiple nodes. Cloud Storage stores datasets, checkpoints, and final model artifacts. The evaluation pipeline runs concurrently with training on separate GKE pods, and Firestore tracks experiment metadata for reproducibility. Pub/Sub orchestrates pipeline stages with failure retry.
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Fine-Tuning Pipeline
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