Hello, This is a PSA - There's an upcoming Webinar tonight, the 17th of June, on the principles of Software Engineering in Machine Learning conducted by Hasgeek from 7 PM - 8.10 PM. In this, Venkata Pingali <https://www.linkedin.com/in/pingali/> and Indrayudh Ghoshal <https://www.linkedin.com/in/indrayudhghoshal/> from Scribble Data <https://www.scribbledata.io/> discuss with Dmitry Pretrov <https://www.linkedin.com/in/dmitryleopetrov/> and Ivan Shcheklein <https://www.linkedin.com/in/shcheklein/>, co-founders of iterative.ai, on the following pertinent points -
1. Do software engineering principles apply to Machine Learning development and deployment? 2. How is an ML system different from traditional application? 3. How important is data versioning? 4. What are the next logical steps in the development of the data science engineering tool chains? 5. How will the data ecosystem evolve over the next few years? These are interesting times and the topics in question are extremely important for anyone who's interested in the fields. As an alumnus of Scribble Data, I've had a chance to witness first-hand the complexities data - structured or unstructured - can pose and the way disciplined engineering can make a difference in the workflow of an ML system. RSVP here to check it out and attend the webinar :) - https://hasgeek.com/fifthelephant/making-data-science-work-session-3/ Regards Abhiram R <https://abhiramr.com> _______________________________________________ BangPypers mailing list BangPypers@python.org https://mail.python.org/mailman/listinfo/bangpypers
