Introducing Pro-ML – Jesus Rodriguez – Medium

Building machine learning solutions at scale remains an active area of experimentation for most organizations. While many companies are starting their initial machine learning pilots, few have a robust strategy to scale machine learning workflows. This issue is particularly challenging if we consider that, in the current market, machine learning research and development frameworks have evolved disproportionately faster than the corresponding infrastructure runtimes required to scale machine learning programs. With so little guidance available about how to build machine learning solutions at scale, an invaluable source becomes the experience of internet giants such as Uber, LinkedIn, Google, Netflix or Microsoft whose scalability requirements are exceedingly more complex than the ones faced by most companies. At LinkedIn, the roadblocks for delivering machine learning solutions at scale were becoming so critical that the company decided to create a separate initiative called Productive Machine Learning(Pro-ML) to address this challenge.