In this video, Michael Karasick from IBM describes how to successfully manage the “Four V’s” of Big Data: Volume, Velocity, Variety and Veracity of data, and how to harness and draw insights from it.
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Why We Need ML Ops: 4 Things to Consider When Testing AI
In this special guest feature, Stephan Jou, CTO of Interset, a Micro Focus company, explores things businesses should consider when deploying production ML pipelines and testing AI. MLOps is important because businesses have now accepted the value of AI and ML, and are now focused on extracting the promised value from those ML systems.
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insideBIGDATA Guide to Optimized Storage for AI and Deep Learning Workloads
This new technology guide from DDN shows how optimized storage has a unique opportunity to become much more than a siloed repository for the deluge of data constantly generated in today’s hyper-connected world, but rather a platform that shares and delivers data to create competitive business value. The intended audience for this important new technology guide includes enterprise thought leaders (CIOs, director level IT, etc.), along with data scientists and data engineers who are a seeking guidance in terms of infrastructure for AI and DL in terms of specialized hardware. The emphasis of the guide is “real world” applications, workloads, and present day challenges.
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