Diagram showing what's needed to build a machine learning product
Technical Insights

Why AI Projects Fail, Part 3: Robustness

Building AI systems is hard. Despite all the hype, AI engineers struggle with difficult problems every day. For the next few weeks, I’ll explore some of the major challenges. Today’s topic: The challenge of building AI systems that are robust to real-world conditions.
Illustration of a ghost
Technical Insights

Why AI Projects Fail, Part 2: Uncommon Pitfalls

Welcome to the Halloween edition of The Batch! I promised last week to share some common reasons for AI project failures. But first, let’s start with some of the least common reasons.
Pie & AI and IASI AI cupcakes
Technical Insights

Why AI Projects Fail

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!
"No silver bullet" book cover
Technical Insights

Reducing Essential Complexity

Thinking about the future of machine learning programming frameworks, I recently reread computer scientist Fred Brooks’ classic essay, “No Silver Bullet: Essence and Accidents of Software Engineering.” Three decades after its initial...

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