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Data Science: Is it Time for Professionalisation?
Machine learning methods and software are becoming widely deployed. But how are we sharing expertise about bottlenecks and pain points in deploying solutions? In terms of the practice of data science, we seem to be at a similar point today as software engineering was in the early 1980s. Best practice is not widely understood or deployed. In this talk we will focus on two particular components of data science solutions: the preparation of data snd the deployment of machine learning systems.
given family url institute twitter gscholar orchid
Neil D.
Lawrence
Amazon Cambridge and University of Sheffield
lawrennd
r3SJcvoAAAAJ
2018-09-05
talk
a4paper
margin=2cm
a4paper
None
notes

\include{_ml/includes/what-is-ml.md} \include{_ml/includes/data-science-vs-ai.md} \include{_ai/includes/embodiment-factors.md} \include{_data-science/includes/evolved-relationship.md} \include{_ml/includes/what-does-machine-learning-do.md} \include{_ml/includes/deep-learning-overview.md} \include{_data-science/includes/a-time-for-professionalisation.md} \include{_data-science/includes/the-data-crisis.md}

\newslide{Rest of this Talk: Two Areas of Focus}

  • Reusability of Data

  • Deployment of Machine Learning Systems

\include{_data-science/includes/data-readiness-levels.md} \include{_data-science/includes/data-joel-tests.md} \include{_ai/includes/deploying-ai.md} \include{_ai/includes/ml-systems-design-long.md}

\section{Conclusion} \newslide{Conclusion} \slides{

  • Artificial Intelligence and Data Science are fundamentally different.
  • In one you are dealing with data collected by happenstance.
  • In the other you are trying to build systems in the real world, often by actively collecting data.
  • Our approaches to systems design are building powerful machines that will be deployed in evolving environments. } \notes{Artificial Intelligence and Data Science are fundamentally different. In one you are dealing with data collected by happenstance. In the other you are trying to build systems in the real world, often by actively collecting data. Our approaches to systems design are building powerful machines that will be deployed in evolving environments. There is an urgent need for new ideas and methodologies for safe deployment and redployment of these systems.}

\thanks

\references