BoF on ML platforms
On machine learning platforms, journeys in building them, and managing infrastructure for ML platforms
The purpose of this BoF is to have a conversation around platforms that
organizations are building develop and deploy ML models. We will discuss a
number of practical challenges in developing and deploying ML Platforms
We will touch upon :
(a) Whether organizations need one and when?
(b) What should it achieve? What is it value proposition?
(c) What is it relationship to cloud offerings such Azure ML?
(d) How should one go about developing one?
(e) How should one think about technology/other choices?
(f) What the challenges in developing and operating one?
Specifically we will discuss
(a) Data flows - stability, scaling, changing requirements
(b) Team structure/skill requirements and availability
(c) Development Support - Notebooks, production vs test, realtime vs batch
(d) Life cycle management - Planning, deployment, evolution
(e) Operations - Monitoring, debugging, evolution to latest tooling
(f) Pressures - Balance of need to deliver vs need to architecture
(g) Processes - For development efficiency, correctness
(h) Data Governance - access and data copy management, privacy
(i) Scaling - how to grow with data sets, number of models, computational requirements, diversity?
Interest in productionization of machine learning
- Krupal Modi, Director of Machine Learning, Haptik
- Subir Mansukhani, India Head, Domino Data Labs
- Ravi SK, Sr. Architect, Walmart Labs
- Soumya Simanta, Principal Architect, Swiggy
- Venkata Pingali, CEO, Scribble Data (Moderator)