Managing Machine Learning Projects - From Design to Deployment

Overview

Explore the intricacies of managing machine learning projects in this insightful 45-minute MLOps Coffee Session featuring Simon Thompson. Gain valuable insights into the multifaceted aspects of ML project management, from setting up and organizing projects to dealing with data assets, addressing business problems, and integrating models into production. Learn from Thompson's extensive experience spanning various industries since 1994, including telecom, defense, manufacturing, and finance. Discover key takeaways on orchestrating different project activities, avoiding common pitfalls, and navigating the evolving landscape of machine learning. Delve into discussions on infrastructure changes, open-source developments, ethical considerations, and scaling challenges in ML projects. Understand the importance of balancing model accuracy with trustworthiness across different markets and get a glimpse into Thompson's book on managing machine learning projects from design to deployment.

Syllabus

[] Simon's preferred coffee
[] Introduction to co-host Abi Aryan
[] Introduction to Simon Thompson
[] Takeaways
[] Simon's background
[] Subscribe to our Newsletter and join our Slack channel!
[] Managing Machine Learning Projects From design to deployment book
[] Simon's inclination toward computers and engineering
[] Simon's first computer project
[] Simon's plan toward computers and engineering
[] Unexpected changes in Machine Learning
[] Changes in infrastructure
[] Change in open source
[] Setting up and organizing Machine Learning Projects
[] Requirements before starting Machine Learning Projects
[] Management big challenges
[] Fundamental value to get to the money
[] Towards ethics
[] Ability to scale
[] Must-haves and nice-tabs
[] Model accuracy and trustability change in different markets
[] Inspiration behind Simon's book
[] Expected conversations about the book
[] There will be free books!
[] Connect with Simon!