MadeWithML – A practical approach to learning production machine learning
> How come there's a lot of buzz around ML but we grossly fall short when it comes to deployed ML in real world applications (especially in certain industries). There are many reasons for this, including but not limited to:> data: not enough of the right data.> cost: the data, compute, storage and talent resources needed for ML allow mostly large companies to benefit from it.> utility: most ML content you see online are gimmicky tasks that can't extend into valuable real world applications.> trust: ML needs to be engineered reliably and robustly.This is a very honest take on ML / AI. I work in blockchain and what drives me crazy is that crypto / blockchain has delivered huge, tangible successes and value (measured easily and accurately via real-time prices) but the HN cognoscenti orgasms over ML and considers blockchain a scam.IMO the situation is flipped - crypto is proving its success in a massive, revolutionary way while ML / AI is pretty stagnant at delivering world-changing products. It's cool that bots are sorting and picking at the warehouse but the value has been overstated by many orders of magnitude (thus far).ML is a tool in a box right now, one of many that can potentially solve a class of problems, but it isn't (yet) a paradigm shift in how businesses operate.