Your synthetic data pipeline is about to break [here's why]

Synthetic data pipelines are becoming more complex and resource-intensive as AI applications advance, requiring significant infrastructure investments. Challenges include handling large datasets, integrating diverse data types, managing real-time data streams, ensuring data quality and consistency, and maintaining scalability and performance. Modernizing with a multimodal lakehouse for storage and the PARK stack (PyTorch, Anyscale, Ray, Kubernetes) for compute can help manage these complexities without needing a dedicated ML team. Notion's use of Anyscale-managed Ray services exemplifies efficient scaling for vector embeddings at scale.

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