Economies of Data Science

Economies of Data Science

Building ties between ML and business



Saurav Raj


Sharmili Srinivasan

Sharmili Srinivasan


Economies of Machine Learning systems

Submitted Jan 23, 2023


Machine Learning (ML) in production is complex due to higher operation costs. For a large-scale serving, the ML-based use cases require an infrastructure of powerful CPUs and GPUs that contributes significantly to an organization’s OpEx. In this session, we explore the ways of reducing the cost of doing ML and the factors that direct our preference for one ML solution over another. By analyzing a real-world business use case the talk would provide our outlook towards -

  • Choosing managed services from public clouds versus developing an ML solution in-house
  • Factors relevant to the ML landscape that affect this choice
  • A cost-effective ML serving infrastructure design




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