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UID:pretalx-europython-2023-PNYMHE-0@programme.europython.eu
DTSTART;TZID=CET:20230717T093000
DTEND;TZID=CET:20230717T110000
DESCRIPTION:What's this thing called **MLOps**? You may have heard about it
  by now\, but never really understood what all the fuzz is about. Let's fi
 nd out together!\n\nIn this tutorial\, you will learn about MLOps and take
  your first steps in a hands-on way. To do so\, we will be using **Open So
 urce** tooling. We will be taking a simple example of Machine Learning use
  case and will gradually make it more ready for production 🚀.\n\nWe sta
 rt with a simple time-series model in Python using scikit-learn and first 
 add logging steps to make the performance of the model measurable. Don't w
 orry: we will go through it step-by-step\, so you won't be overwhelmed. Th
 en\, we will log our ML model and load it back into an inference step. Las
 tly\, we will learn about deploying these actual models by Dockerizing our
  application 🙏.
DTSTAMP:20260513T153503Z
LOCATION:Club E
SUMMARY:How to MLOps: Experiment tracking & deployment 📊 - Jeroen Oversc
 hie\, Yke Rusticus
URL:https://programme.europython.eu/europython-2023/talk/PNYMHE/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-europython-2023-PNYMHE-1@programme.europython.eu
DTSTART;TZID=CET:20230717T111500
DTEND;TZID=CET:20230717T124500
DESCRIPTION:What's this thing called **MLOps**? You may have heard about it
  by now\, but never really understood what all the fuzz is about. Let's fi
 nd out together!\n\nIn this tutorial\, you will learn about MLOps and take
  your first steps in a hands-on way. To do so\, we will be using **Open So
 urce** tooling. We will be taking a simple example of Machine Learning use
  case and will gradually make it more ready for production 🚀.\n\nWe sta
 rt with a simple time-series model in Python using scikit-learn and first 
 add logging steps to make the performance of the model measurable. Don't w
 orry: we will go through it step-by-step\, so you won't be overwhelmed. Th
 en\, we will log our ML model and load it back into an inference step. Las
 tly\, we will learn about deploying these actual models by Dockerizing our
  application 🙏.
DTSTAMP:20260513T153503Z
LOCATION:Club E
SUMMARY:How to MLOps: Experiment tracking & deployment 📊 - Jeroen Oversc
 hie\, Yke Rusticus
URL:https://programme.europython.eu/europython-2023/talk/PNYMHE/
END:VEVENT
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