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#016 Data Processing for AI, Integrating AI into Data Pipelines, Spark

How AI Is Built

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Challenges and Strategies for Integrating AI into Data Pipelines

This chapter delves into the complexities of integrating AI into data pipelines, focusing on challenges such as unstructured data processing, infrastructure setup, and forward compatibility. It discusses the importance of efficient backend systems, Apache Arrow integration for scalability, monitoring tools like Datadog and Grafana, and the significance of vector databases for semantic search. The speakers also address issues related to latency, error handling, and retries in data processing, emphasizing the need for optimization and fallback mechanisms in production environments.

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