Making Data Science Work session 3

Applying software engineering principles to Machine Learning projects


About

The now famous NeurIPS Technical Debt Paper from 2015 discussed how most of the code and effort in the production machine learning system is non-ML engineering and data management work.

In this forum, hosts Venkata Pingali and Indrayudh Ghoshal of Scribble Data will chat with Dmitry Pretrov, co-founder iterative.ai, and Ivan Shcheklein, co-founder of iterative.ai to discuss:

  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?

References
1. DVC
2. DVC Ambassador Program

Register to participate via Zoom. Zoom link will be shared one day before the event. Or, watch the livestream on this page.
Registered participants can also leave comments and questions for the hosts and speakers, which will be taken up during the session.

Previous session: The previous session was held on 3 June May. Summary of the session is available on https://hasgeek.com/fifthelephant/making-data-science-work-2/

About Scribble Data: Scribble Data is a Bangalore/Toronto startup and an active member in the data community. Scribble implements MLOps for Data using their customizable feature store, Enrich.

About the organizers: The Fifth Elephant is a platform for practitioners working with data (anywhere from ingestion, to its application in data science for different use cases) to showcase their work and to collaborate.

For further inquiries, contact 7676332020 or write to fifthelephant.editorial@hasgeek.com

Host

Scribble Data