There’s Fire Behind That Smoke
Machine learning and AI are getting a lot of press lately, but it it is more than just hype. Applications and systems that use ML/AI are generating serious amounts of value, often in very surprising applications. But using these new techniques to build systems doesn’t get you a free pass on basic engineering issues. To get a practical and sustainable value you will have to connect these systems to real problems whose solution has real business value. To succeed in this, you have to have a repeatable engineering process that can deploy these ML/AI models reliably.
In many ways, there are strong analogies with software development processes, but with important differences. You have to have version control (but it is different with data), continuous deployment (but this is trickier with models), and automated testing (but that is harder when we are learning a model rather than coding a program).
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