Unfortunately, scaling machine learning is not as simple as taking what worked in the pilot phase and adding more sensors. "Pilot programs are an important first step, but expanding and unifying pilot programs is the next phase in the adoption cycle." Infrastructure decisions will become increasingly important as you grow your machine learning. The best way to scale well is to work with the experts!
In a world of cars that drive themselves and robots that do back flips, the fully automated industrial operation remains an aspiration. Limitations in real-time computing, AI and robotics slow progress, regardless of how advanced a company is or the size of its data science team. Even Elon Musk, who has some of the biggest ambitions out there, can only automate Tesla factories piece by piece. Bottom line: Even though some level of automation at scale is attainable, many industrials find themselves stuck at the pilot project phase. A combination of short-staffed data-science teams, limited data and limited ability to quickly affect business processes stymies expansion. But with a new, open-source way of thinking, industrials can drive digital operations at a pace similar to the large tech companies. Read More
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