The Data Bottleneck: How Modern ML Systems Fail and How to Build Ones That Dont
Data science has shifted from a compute-centric discipline to a data-intensive, systems-oriented one. It explains why traditional modeling skills are no longer enough and why production success depends on understanding data pipelines, reliability engineering, scalability constraints, distributed systems behavior, maintainability, and real-world fault tolerance. It reframes the role of the data scientist as a builder of resilient sociotechnical systems with an emphasis on operability, latency, robustness, and long-term evolution.
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