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Economies of Data Science

Economies of Data Science

Building ties between ML and business

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The topics that we will focus in this stream are: How data science teams measure the performance of a model? What are the Key Performance Indicators (KPIs)? Do data scientists who designed the model see the ML approach as a black box? expand

The topics that we will focus in this stream are:

  1. How data science teams measure the performance of a model?

    • What are the Key Performance Indicators (KPIs)?
    • Do data scientists who designed the model see the ML approach as a black box?
  2. How businesses evaluate the impact of the ML models?

    • How do you measure the Return of Investment (RoI)?
    • How are you increasing the efficiencies in a business landscape?
  3. How is Observability implemented in your organization?

  • Can you elaborate on the tools, processes and metrics used?
  • How are you increasing the usage time of an application for a user?
  1. What are your infrastructure costs?

    • For data heavy ML models, how do you plan long-term infrastructure investments?
    • How do you reduce the cost of operating and managing systems?
  2. What has been the customer feedback?

    • Are the customers finding it difficult to interpret data shown to them?
    • Do you see a high correlation in the business?
  3. How are you measuring and improving customer experience?

    • Do you still work on augmenting reality for users?
    • Are you still continuing to use advertisment targeting based on personal preference and usage?

Preference will be given to case study talks, and talks that have practical relevance for practitioners.

Saurav Raj

Sharmili Srinivasan

Sharmili Srinivasan

Economies of Machine Learning systems

Abstract 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 an… more
  • 0 comments
  • Submitted
  • 23 Jan 2023

Chaitanya Sangani

Productivity Hacks for small ML teams

What is the problem? For an ML team, “small” usually means having limited resources, manpower, and budget compared to larger teams. While small teams can be more agile, collaborative and focused, overreliance on one or a few individuals, low work reproducibility affects and no standardisation in code development affects the team’s performance. more
  • 1 comment
  • Submitted
  • 13 Feb 2023
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