Real-Time Machine Learning Challenges

Building real-time machine learning (ML) capabilities is challenging due to the need to maintain service levels, handle edge cases, and ensure reliability in production pipelines. Common challenges include building reliable streaming pipelines that can account for data skew issues, spiky throughput, and managing internal state stores while maintaining low latency and feature freshness. Ensuring uptime, availability, and meeting specific service-level agreements (SLAs) is also a significant operational burden. Additionally, mitigating training/serving skew, which refers to model performance issues due to outdated or inconsistent data, requires careful inspection of transformation logic and detection of data drift. Companies often turn to feature stores or platforms to solve these challenges, as seen in examples like CashApp and Instacart's use of feature platforms and in-house machine learning capabilities.

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