It’s pretty much understood that companies in most every industry will need to implement some level of machine learning in order to remain competitive.With the vast amounts of data companies accumulate, they need to make this data work for them—helping them predict the likelihood of a loan going into default, what fashion trends customers will be looking for next summer, or how many buses may be operating in specific regions and their impact on traffic.Before machine learning this was stuff that was close to impossible to determine with any level of accuracy.[ The InfoWorld review roundup: AWS, Microsoft, Databricks, Google, HPE, and IBM machine learning in the cloud. | Cut to the key news and issues in cutting-edge enterprise technology with the InfoWorld Daily newsletter. ]Data is the lifeblood of machine learningCreating a machine-learning algorithm that enables software to conduct this type of predictive analysis just doesn’t happen overnight. It’s all about sorting through vast amounts of data (yes, big data), labeling it and cleaning it to build and train and re-train an algorithm that can help it identify precisely what you are hoping to find.Accomplishing this can certainly be a tedious process, literally pouring through tons of data, to simply mark specific things. To complete the process as quickly as possible, most AI experts have used the power of many “bodies,” who, as long as they have eyes to identify objects or text and label it, are put on the job.They’ve also used the power of the masses to…more detail
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