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UID:9f894dbc-0c26-4ac6-a21f-d01836e6215d@support.access-ci.org
DTSTAMP:20251121T121832Z
DTSTART:20260310T180000Z
DTEND:20260310T193000Z
SUMMARY:Architecting Reproducible Science: A Practical Path Beyond the Note
 book
DESCRIPTION:SummaryJupyter Notebooks are, for the most part, useful for fas
 t experimentation. However, poor programming practices can lead you into t
 he unreproducible-code trap, which is fatal for your research goals. In th
 is talk, we will guide you through a practical and swift process to avoid 
 this trap and migrate your notebook code to a nice, neat little reproducib
 le Python package. This, you will see, will allow you to improve testing, 
 automation, reproducibility (of course), and enhance your long-term scient
 ific workflows overall. We will cover important Software Engineering funda
 mentals: separating concerns, organizing modules, adding environment contr
 ols, and enabling CLI executions for HPC environments. You will witness th
 e game-changing benefits that Python packages provide, among which reprodu
 cibility is the most valuable for long-term research work. We will also di
 ve into highly profitable fields of work where these techniques are used e
 xtensively to improve ML pipelines (also known as MLOps), which enable com
 panies to deliver substantial amounts of value to their customers at a sup
 er fast pace. As you will see, dominating this set of skills will position
  you as a very attractive candidate for a substantial number of companies 
 and startups in this hot and crazy AI market that we are living in today.I
 nstructorFernando Garzon works as a Data Scientist and Software Engineer a
 t the San Diego Supercomputer Center (SDSC) since August 2022. Although hi
 s background is in Physics, he transitioned into Scientific Computing and 
 Software Engineering, focusing on building reproducible, scalable workflow
 s for research and HPC environments. His work spans data engineering, back
 end development, distributed systems, and modern AI/ML pipelines. Fernando
  is also part of the Open Science Chain project, where he helps design and
  implement secure, high-performance architectures using Python, TypeScript
 , and blockchain technologies. He is passionate about bridging the gap bet
 ween exploratory research code and production-ready scientific software.Se
 e a complete list of SDSC's upcoming training and events here.
URL:https://support.access-ci.org/events/8713
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