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A few years back, you would have not heard the word "scalable" in machine learning parlance. The reason was mainly attributed to the lack of infrastructure, data, and real-world application. Machine learning was being much talked about in the research community of academia or in well-funded industry research labs. A prototype of any real-world application using machine learning was considered a big feat and a demonstration of breakthrough research. However, time has changed ever since the availability of powerful commodity hardware at a reduced cost and big data technology's widespread adaption. As a result, the data has become easily accessible and software developments are becoming more and more data savvy. Every single byte of data is being captured even if its use is not clear in the near future.
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