Deep Dive – HOME
MLOps and DevOps: Why Data Makes It Different
Machine Learning’s deployment stack is maturing
Ethical Social Media: Oxymoron or Attainable Goal?
It's Time to Look More Closely at Regulation
A Way Forward with Communal Computing
Do’s and Don'ts when Designing for the Community
Defending against ransomware is all about the basics
Authentication, Backups, Updates, and Least Privilege
Communal Computing’s Many Problems
Where user-centric computing goes wrong
Thinking About Glue
The code that holds our systems together
Hand Labeling Considered Harmful
Labeling training data is the one step in the data pipeline that has resisted automation. It’s time to change that.
Communal Computing
How do we build devices that are shared by default?
AI Powered Misinformation and Manipulation at Scale #GPT-3
Risks of autoregressive language models and the future of prompt engineering
The End of Silicon Valley as We Know It?
Four ways the party may be coming to an end
Product Management for AI
Seven Legal Questions for Data Scientists
AI Product Management After Deployment
The AI product manager’s job isn’t over when the product is released. PMs need to remain engaged after deployment.
How to Set AI Goals
Identifying AI opportunities and setting appropriate goals are critical to AI success, and yet can be difficult to do in practice. Some reasons for this include lack of AI literacy, maturity, and many other factors.
Why Best-of-Breed is a Better Choice than All-in-One Platforms for Data Science
All-in-one platforms built from open source software make it easy to perform certain workflows, but make it hard to explore and grow beyond those boundaries.
Bringing an AI Product to Market
Previous articles have gone through the basics of AI product management. Here we get to the meat: how do you bring a product to market?
Automated Coding and the Future of Programming
How automation is likely to change professional software development.