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.
The technical AI chasm
New data. Data is key to AI. For example, if you want a chatbot to learn, you have to feed its algorithm with examples of customer requests and the corresponding correct responses. Such data are often presented in a well-structured, but static, format such, as CSV files.
While you can build cool AI demos using static data sets, real-world AI that runs machine learning algorithms will constantly need new data to become smarter over time. That’s why companies should invest early in machine learning architecture that continually collects new data and uses it to regularly update its AI models.
The use of live data presents numerous engineering challenges, including scheduling, zero-downtime model updates, stability and performance monitoring. Plus, you need a mechanism to roll back to a previous state if something goes wrong with your new data. That leads us to the next point.
Read the source article at TechCrunch.