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UID:pretalx-pycones2022-F3V98A@charlas.2022.es.pycon.org
DTSTART;TZID=CET:20221001T160500
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DESCRIPTION:In this talk\, we will share some of the lessons we have learne
 d over the last 4 years that we have been developing ML solutions\, and de
 ploying and maintaining them in production in real enterprise operations.\
 n\nWith regard to ML solution development\, we will share our insights so 
 far on overcoming some challenges that we have seen commonly arise in a pr
 ocess typically driven by iterative experimentation within a team\, focusi
 ng above all on achieving high levels of traceability and reproducibility.
  Combining various common MLOps best practices such as versioning data and
  models together with code\, as well as tracking experiments\, we have set
  up a methodology that makes it practically impossible for team members to
  evade conducting their work in a highly reproducible way\, at the same ti
 me as providing flexibility for rapid experimentation.\n\nWhen it comes to
  deployment\, the MLOps practices that have served us particularly well ar
 e the principles of early and controlled deployment (shadow mode\, canary 
 and blue-green deployments)\, the careful definition of key business and t
 echnical metrics\, and an obsessive focus on observability and monitoring.
  We will also touch upon some non-technical challenges that we have common
 ly encountered along the way.\n\nAside from sharing our own experiences an
 d lessons learned\, we would like to encourage a constructive discussion w
 ith the community\, drawing on the wide experience of the community to con
 tinue to evolve best practices in the field.
DTSTAMP:20260306T061933Z
LOCATION:Katherine Johnson (Teoría 7)
SUMMARY:Fail Fast MLOps: Lessons learned from deploying ML solutions in pro
 duction - Vasja Urbancic
URL:https://charlas.2022.es.pycon.org/pycones2022/talk/F3V98A/
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