2017 Oil & Gas HPC Conference

TWO MINUTE LIGHTENING TALK

Numerous data points quantify the current, significant level of interest in Machine Learning - and, when Big Data is involved, Deep Learning. And although Machine Learning is already receiving some degree of uptake in the oil and gas industry, as its value is validated in proof-of-concept initiatives, adoption across the enterprise presents additional opportunities and challenges. Because Machine Learning has the potential to contribute to numerous, existing workloads and workflows to varying degrees, a departure from classic architectural patterns may be warranted. In particular, it is argued that departures that seek to introduce Machine Learning capabilities as a micro service warrant attention. To fix ideas, a specific use case is shared: Machine Learning capabilities from Apache Spark are containerized and scheduled for use via Navops Command by Univa, in a container cluster based on Kubernetes. Starting with use-case examples such as this one, organizations can be better informed and inclined to consider micro services based architectures as they adopt and adapt Machine Learning capabilities into their existing workloads and workflows.