Hein Lu @Linked in, Brad Mitro @GoogleJeff Smith @ Facebook

For other QCon blog posts, see QCon live blog table of contents

Getting Started

• People with other strong IT skills switched over
• Can learn from books, coursera,, udacity, grad school
• Look for specific applications
• Domain is very large
• Learn libraries, existing datasets
• Understand where organization is at. Ex want to do ML vs specific problem
• Focus on how will deliver business value

General

• Many problems repeat so can get ideas from others
• Important to have organizational alignment
• Make sure to train on realistic data
• Deep learning is very successful use case of ML
• ”AI is the new electricity”
• Limits of Moore’s law. Physical limitations with Quantum
• Research on how to get algorithms to train theselve

Tools

• PyTorch Hub

Learning resources

• Jeff’s book – Machine Learning Systems
• Andrew Ng’s Coursera ML course
• Coming out this year “AI is for everyone”

Q&A

• How learn without business case? How know what don’t know? Many educational resources start generally. Can skip some core concepts and learn later.
• How pick good training data? Iterate on testing. Important to keep training with new data
• Data heurisitcs? How much data? How many labels?
• How make more agile? Use a pretrained model to start. Exist as a service or pull in via code
• How know when good enough? Sometimes you have to just try. Or look to those who solved similar problems
• Tech stack? Hardware acceleration. Iibraries
• Fraud? Retrain data

My impressions

This was a good panel. Interesting responses. One panelist was missing, but it came out well