Building Machine Learning Powered Applications
Going from Idea to Product
by Emmanuel Ameisen
Should I read this?
Recommended by 1 source and appears in Machine Learning and Data Science.
Learn the skills necessary to design, build, and deploy applications powered by machine learning. Through the course of this handson book, you'll build an example MLdriven application from initial idea to deployed product. Data scientists, software engineers, and product managers with little or no ML experience will learn the tools, best practice...
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Why recommended
Recommended by 1 source and appears in Machine Learning and Data Science.
Recommended by notable people
People and public figures who have recommended this book.
Recommendation Signals
Recommendation proof is sourced from public posts, interviews, reading lists, and cited references.
Monica Rogati
“Before starting to code an ML algorithm, spend one hour trying to do its job. Be the algorithm. Learned this early on from @IBMResearch's Salim Roukos. You can read more in the @mlpowered book, which is full of practical ML advice missing from textbooks:”
Appears In
Not sure if this is the right fit?
Consider Life 3.0 by Max Tegmark. Recommended by 18 sources.
“Life 3.0 reads like a long, wide-ranging conversation with a physicist who loves big if-then thought experiments. The useful part is its panoramic sweep across possible AI futures—from job automation to cosmic colonization—forcing you to consider timelines you might otherwise avoid. The limitation is that the speculative breadth often outruns the depth; chapters can feel meandering, and some readers will find the cosmic-scale scenarios too detached from practical concerns, making it hard to ground in real urgency.”
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Each recommendation is collected from a public source — interviews, articles, or curated lists — and linked to its original URL. Books with many verifiable recommendations from respected people rank higher.