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Don't fall for the AI hype: Here are the ingredients you need to build an actual useful thing

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Artificial intelligence these days is sold as if it were a magic trick. Data is fed into a neural net – or black box – as a stream of jumbled numbers, and voilà! It comes out the other side completely transformed, like a rabbit pulled from a hat. That's possible in a lab, or even on a personal dev machine, with carefully cleaned and tuned data. However, it is takes a lot, an awful lot, of effort to scale machine-learning algorithms up to something resembling a multiuser service – something useful, in other words.


10 Ways Machine Learning Is Revolutionizing Manufacturing

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Bottom line: Every manufacturer has the potential to integrate machine learning into their operations and become more competitive by gaining predictive insights into production. Machine learning's core technologies align well with the complex problems manufacturers face daily. From striving to keep supply chains operating efficiently to producing customized, built- to-order products on time, machine learning algorithms have the potential to bring greater predictive accuracy to every phase of production. Many of the algorithms being developed are iterative, designed to learn continually and seek optimized outcomes. These algorithms iterate in milliseconds, enabling manufacturers to seek optimized outcomes in minutes versus months.


How To Get Better Machine Learning Performance

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Machine Learning Performance Improvement Cheat Sheet Photo by NASA, some rights reserved. This cheat sheet is designed to give you ideas to lift performance on your machine learning problem. Outcome: You should now have a short list of highly tuned algorithms on your machine learning problem, maybe even just one. In fact, you can often get good performance from combining the predictions from multiple "good enough" models rather than from multiple highly tuned (and fragile) models.


Deploy Your Predictive Model To Production - Machine Learning Mastery

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Often the complexity a machine learning algorithms is in the model training, not in making predictions. I also strongly recommend gathering outlier and interesting cases from operations over time that produce unexpected results (or break the system). Like a ratchet, consider incrementally updating performance requirements as model performance improves. If you're interested in more information on operationalizing machine learning models check out the post: This is more on the Google-scale machine learning model deployment.


Data-First Machine Learning - insideBIGDATA

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In this special guest feature, Victor Amin, Data Scientist at SendGrid, advises that businesses implementing machine learning systems focus on data quality first and worry about algorithms later in order to ensure accuracy and reliability in production. At SendGrid, Victor builds machine learning models to predict engagement and detect abuse in a mailstream that handles over a billion emails per day. The training set (the data your machine learning system learns from) is the most important part of any machine learning system. Instead, build a system that samples production data, and have a mechanism for reliably labeling your sampled production data that isn't your machine learning model.


NON-VON's applicability to three AI task areas

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Proceedings of the Ninth International Joint Conference on Artificial Intelligence, Los Angeles, Calif., pp. 61-70