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.
Few technologies have the potential to change the nature of work and how we live as artificial intelligence (AI) and machine learning (ML).
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.
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.
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?
Data is often biased. But that isn’t the real issue. Why is it biased? How do we build teams that are sensitive to that bias?
A Bad Outcome Doesn't Mean a Bad Decision
Getting curious about the numbers attached to other people can help us to use data wisely—and to see others clearly.
Companies that succeed will protect, fight for, and empower their users
A product manager for AI does everything a traditional PM does, and much more.
Your models are only as good as your data.
O’Reilly usage analysis shows continued growth in AI/ML and early signs that organizations are experimenting with advanced tools and methods.
O’Reilly survey results show that AI efforts are maturing from prototype to production, but company support and an AI/ML skills gap remain obstacles.
Our annual analysis of the O’Reilly online learning platform reveals Python’s continued dominance and important shifts in infrastructure, AI/ML, cloud, and security.
O’Reilly survey highlights the increasing attention organizations are giving to data quality and how AI both exacerbates and alleviates data quality issues.
Edward Jezierski on the science of bringing creativity and curiosity together in a learning system.
Roger Magoulas looks at developments in automation, hardware, tools, model development, and more that will shape (or accelerate) AI in 2020.
Rob Thomas and Tim O’Reilly discuss the AI Ladder framework.
Understanding and fixing problems in ML models is critical for widespread adoption.