
#016 Data Processing for AI, Integrating AI into Data Pipelines, Spark
How AI Is Built
00:00
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.
Transcript
Play full episode