MLOps with vetiver in Python and R | Led by Julia Silge & Isabel Zimmerman

Many data scientists understand what goes into training a machine learning model, but creating a strategy to deploy and maintain that model can be daunting. In this meetup, learn what MLOps is, what principles can be used to create a practical MLOps strategy, and what kinds of tasks and components are involved. See how to get started with vetiver, a framework for MLOps tasks in R and Python that provides fluent tooling to version, deploy, and monitor your models.

Blog Post with Q&A: https://www.rstudio.com/blog/vetiver-...

For folks interested in seeing what data artifacts look like on Connect, we have these for R:
⬢ Versioned model object: https://colorado.rstudio.com/rsc/seat...
⬢ Deployed API: https://colorado.rstudio.com/rsc/seat...
⬢ Monitoring dashboard: https://colorado.rstudio.com/rsc/seat...
⬢ Create a custom yardstick metric: https://juliasilge.com/blog/nyc-airbnb/
⬢ End point used in the demo: https://colorado.rstudio.com/rsc/scooby

Our team's reading list (mentioned in the meetup)

Books:
⬢ Designing Machine Learning Systems by Chip Huyen: https://www.oreilly.com/library/view/...

Articles:
⬢ “Machine Learning Operations (MLOps): Overview, Definition, and Architecture” by Kreuzberger et al: https://arxiv.org/abs/2205.02302
⬢ “From Concept Drift to Model Degradation: An Overview on Performance-Aware Drift Detectors” by Bayram et al: https://arxiv.org/abs/2203.11070
⬢ “Towards Observability for Production Machine Learning Pipelines” by Shankar et al: https://arxiv.org/pdf/2108.13557.pdf
⬢ “The ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction” by Breck et al: https://static.googleusercontent.com/...

Web content:
⬢ How ML Breaks: A Decade of Outages for One Large ML Pipeline by Papasian and Underwood:    • OpML '20 - How ML Breaks: A Decade of...  
⬢ MLOps Principles by INNOQ: https://ml-ops.org/content/mlops-prin...
⬢ Google’s Practitioners Guide to MLOps by Salama et al: https://services.google.com/fh/files/...
⬢ Gently Down the Stream by Mitch Seymour: https://www.gentlydownthe.stream/

Speaker bios:
Julia Silge is a software engineer at RStudio focusing on open source MLOps tools, as well as an author and international keynote speaker. Julia loves making beautiful charts, Jane Austen, and her two cats.

Isabel Zimmerman is also a software engineer on the open source team at RStudio, where she works on building MLOps frameworks. When she's not geeking out over new data science techniques, she can be found hanging out with her dog or watching Marvel movies.