Making Data Science Work session 3
Applying software engineering principles to Machine Learning projects
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. This session will discuss:
- Do software engineering principles apply to Machine Learning development and deployment?
- How is an ML system different from traditional application?
- How important is data versioning?
- What are the next logical steps in the development of the data science engineering tool chains?
- How will the data ecosystem evolve over the next few years?
Panelists:
1. Dmitry Pretrov, co-founder iterative.ai
2. Ivan Shcheklein, co-founder of iterative.ai
References
1. DVC
2. DVC Ambassador Program
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 the curators: Venkata Pingali and Indrayudh Ghoshal of Scribble Data have curated this session. Scribble Data is a Bangalore/Toronto startup, active in the data community.
About the series producer: The Fifth Elephant is platform for practitioners working with data (engineering, to application of 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
Hosted by
The Fifth Elephant - known as one of the best data science and Machine Learning conference in Asia - has transitioned into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more
Hosted by
The Fifth Elephant - known as one of the best data science and Machine Learning conference in Asia - has transitioned into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more