“Enterprise Machine Learning requires looking at the big picture […] from a data engineering and a data platform perspective,” lectured Justin Norman during the talk on the deployment of Machine Learning models at this year’s DataWorks Summit in Barcelona. Indeed, an industrial Machine Learning system is a part of a vast data infrastructure, which renders an end-to-end ML workflow particularly complex. The challenges linked to the development, deployment, and maintenance of the real-world ML systems should not be overlooked as we pursue the finest ML algorithms.
Machine Learning is not necessarily meant to replace human decision making, it is mainly about helping humans make complex judgment base decisions.
The talk I attended, Machine Learning Model Deployment: Strategy to Implementation, was given by Cloudera’s experts, Justin Norman and Sagar Kewalramani. They gave a presentation on the challenges encountered by an end-to-end ML workflow, focusing on delivering Machine Learning to production.
This article was originally published by Adaltas and was written by Oskar RYNKIEWICZ.
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