Business challenges and risks related to MLOps

MLOps for Business

Arne Jonas Warnke

Head of Emerging Curriculum

Agenda of chapter 3

Video Business challenges and risks related to MLOps:

  • Business challenges and risks related to MLOps

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Video How MLOps teams successfully operate:

  • How MLOps teams successfully operate

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Video The state of MLOps today

  • The state of MLOps today
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MLOps is challenging

$$ Many companies struggle

  • To operationalize
  • To automate design and development
  • To streamline and scale

their ML use cases

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Challenge

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MLOps requires diverse skills

MLOps lies at the intersection of Machine Learning, Data Engineering, and Software Engineering

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MLOps challenges: skills

Teams often

  • Lack certain necessary skills
    • Often related to software engineering

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That can lead to

  • Technical debt
  • Lack of standardization
  • Insufficient reproducibility

People working together to solve a puzzle

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MLOps challenges: collaboration and culture

Collaboration is key to ensure

  • Statistical metrics & business are aligned
  • All stakeholders understand how MLOps teams operate

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Culture and habits of

  • Constant learning
  • Acknowledgement that failures happen
  • Documentation and knowledge sharing

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People collaborating in front of a computer

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MLOps challenges: technology

Technology

  • Evolves fast
  • No common MLOps tool stack
    • Different teams use widely different technological approaches
    • Few widely accepted best practices regarding technology

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Common mistake

  • Over-emphasis on technology

$$ An intelligent network graph

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MLOps challenges: risks

Risks of operating machine learning models:

  • Business and financial risks
    • Application/model might not be available
    • Prediction quality might deteriorate
    • Maintenance risk
  • Governance risks
    • Prediction might be completely off
    • Possible bias
  • (Cyber) Safety risks

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A illustration of different risks

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Traditional software projects vs. MLOps

Traditional software

  • Testing, debugging ...

MLOps

  • Testing, debugging ...
  • Data monitoring
  • Model output monitoring
  • ...

$$ A human writing code

$$ A hybrid human-robot writing code

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Let's practice!

MLOps for Business

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