I’ve heard this conversation in multiple companies:
- Machine learning engineer: Look how well I did on the test set!
- Business owner: But your ML system doesn’t work. This sucks!
- Machine learning engineer: But look how well I did on the test set!
Why do AI projects fail? Last week, I addressed this question at our Pie & AI meetup. We had a spirited discussion with a live audience in 10 cities from London to Berlin, Ghent (Belgium) to Logroño (Spain).
I remain as optimistic as ever about the AI industry, but I also see many AI projects struggle. Unlike software engineering, the process of engineering AI systems is immature, and teams have not yet learned about the most common pitfalls and how to avoid them.
Common pitfalls fall under the headings: robustness, small data, and workflow. You can increase your odds of success by analyzing your AI project in terms of these issues. I’ll flesh out my thoughts on this in coming weeks. Stay tuned.
Read part 2 of this series now.
Read part 3 of this series now.
Read part 4 of this series now.