Leveraging ML Ops to Enhance Your Data Science Factory -- Quickpath

Machine learning falls into a category of technology currently experiencing hyper-exponential growth as enterprises capitalize on its ability to transform data into insightful action. Like any hyped technology, machine learning is not without current limitations; however, companies operating on the cutting-edge are finding innovative ways to integrate machine learning into an impressive bottom-line. And it's no longer just pet projects for elite Fortune 500 brands--everyone is joining the fun. With this rapid evolution, the role of data professionals is being reconceptualized; leaders increasingly understand data-related infrastructure in terms of the factory model, giving rise to the notion of a data science factory. Data goes in, actionable insights come out, and everything happening in-between falls into the "making the sausage" category--nobody wants to know too much. On one end of the factory, you have the dizzying collection of platforms to manipulate data (Python, H2O, TensorFlow, R, Scikit-Learn, Keras, SAS, Openface, Caffe2, Watson, Google, Azure, AWS ML cloud APIs, and the list goes on).