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The Rise of Serverless Databases // Alex DeBrie // MLOps Podcast #147
MLOps.community
Challenges in Developing Machine Learning Applications with Event Driven Architectures
Developing machine learning applications with event-driven architectures poses challenges such as evolving schemas, sharing schemas among producers and consumers, and difficulty in notifying consumers about schema changes. Event-driven architectures are still perceived as more complex and less intuitive compared to request-response APIs. However, event-driven architectures enable scalability, decoupling of components, and reusability of data for machine learning pipelines and data engineering. The debate between batch processing and streaming data involves concerns about handling mutable data, updates, and data quality. While streaming is promising, batch processing is preferred for maintaining data quality and dealing with variable data. Challenges in data engineering include managing production databases, mutable events, and internal analytics for tracking feature usage and deprecating features.
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