A few months ago, my company, CrowdFlower, ran a machine learning competition on Kaggle. It perfectly highlighted the biggest opportunity (and challenge) with machine learning: What do you do with an 80% accurate algorithm? We uploaded data collected on our platform and Kaggle sent it out to over 1,000 data scientists, who competed to see who could build the best search model. The simplest approach gave a baseline accuracy of 32%. By the next morning, one team already had a 53% accurate model.

How can businesses integrate imperfect machine-learning algorithms into their workflow? With Microsoft's Azure ML and IBM's investment in Watson, making models is easier than ever. Companies no longer need a Google-size R&D budget to make machine learning applicable to their business. The new challenge for businesses is how to integrate an imperfect machine-learning algorithm into their existing workflow. Original Post Bio: Lukas Biewald is the co-founder and CEO of CrowdFlower.

A few months ago, my company, CrowdFlower, ran a machine learning competition on Kaggle. It perfectly highlighted the biggest opportunity (and challenge) with machine learning: What do you do with an 80% accurate algorithm? We uploaded data collected on our platform and Kaggle sent it out to over 1,000 data scientists, who competed to see who could build the best search model. The simplest approach gave a baseline accuracy of 32%. By the next morning, one team already had a 53% accurate model.