Vinay Kakade
Vinay is currently the Founder and CEO of Abhyasu.com, an ed-tech initiative to teach Python and AI to 10-14 year old kids. Previously, he architected ML Platform at Lyft, co-founded a startup that was acquired by Lyft, built AWS Elasticsearch and CloudSearch services, and built Amazon.com's product search engine.
Vinay holds MS in Computer Science from Stanford University, USA, and BE in Computer Engineering from the College of Engineering, Pune, India.
Sessions
Summary
Today, Jupyter Notebooks are mostly confined to science, research & education. But notebooks can provide organizations with a powerful general-purpose “executable documentation” platform. A solid use case for this is DevOps & more specifically, IT incident response.
Technology teams usually have an on-call rotation with static wiki-style documentation to guide the on-call engineer. Jupyter Notebooks can replace static documentation with executable notebooks. E.g. “fetch service logs” and “rollback last deployment” can simply mean executing a code cell that’s available alongside the markdown instructions.
What are the benefits of executable vs. static documentation for DevOps -
- Quick e.g. “check DB latency” is 1-click notebook code cell execution to plot latency graph vs. going to a third-party UI in the middle of an incidence
- Precise e.g. “promote read replica to master” can mean a series of steps & possibility of human error; codifying the steps in advance removes ambiguity & results in precise action.
“Executable documentation” is a simple yet powerful concept that can extend to other use cases such as - API documentation, developer onboarding, data visualization & reporting, scheduling routine tasks & so on. Think of it as executable GoogleDocs powered by Jupyter!
In this talk, we’d like to,
- Introduce the concept of Jupyter powered “executable documentation” platform, particularly for DevOps and Incident Response,
- Show a demo of how it’d work - (https://www.youtube.com/watch?v=vvLXSAHCGF8)
- Talk about important challenges, and propose a way forward to make this a mainstream application of Jupyter notebooks.