Every day brings another exciting story of how artificial intelligence is improving our lives and businesses. AI is already analyzing x-rays, powering the Internet of Things and recommending best next actions for sales and marketing teams. The possibilities seem endless.
But for every AI success story, countless projects never make it out of the lab. That’s because putting machine learning research into production and using it to offer real value to customers is often harder than developing a scientifically sound algorithm. Many companies I’ve encountered over the last several years have faced this challenge, which I refer to as “crossing the AI chasm.”
I recently presented those learnings at ApacheCon, and in this article I’ll share my top four lessons for overcoming both the technical and product chasms that stand in your path.
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