Subject archive for "perspective," page 9

Kubernetes is an open-source container orchestration system that is quickly becoming essential to IT departments as they move towards containerized applications and microservices. As powerful as Kubernetes is with general IT workloads, Kubernetes also offers unique advantages to support bursty data science workloads. With the help of containers, data scientists can make their modeling portable and reproducible, and massively scale these same, containerized machine learning models.

Data science’s impact has become increasingly clear in every industry as leading adopters raise the bar for personalized services and customer experiences. IDC’s recent survey on AI found that early adopters reported a 25 percent improvement in customer experience along with accelerated rates of innovation and higher competitiveness, among other benefits.

This article was originally published to LinkedIn. Thanks to Dan Harris for giving us permission to re-post it here.

Many life sciences organizations are in the midst of digital transformation efforts. A key component of this is moving existing on-premises IT platforms, statistical computing environments (SCEs), and associated programming languages and tools into the cloud. Research and Data Science functions represent low-hanging fruit for these cloud migrations because their workflows necessitate dynamic access to compute infrastructure and centralized access to diverse toolsets.

Building models requires a lot of time and effort. Data scientists can spend weeks just trying to find, capture and transform data into decent features for models, not to mention many cycles of training, tuning, and tweaking models so they’re performant.