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Interview: Global Technology Leader PNY

The following whitepaper download is a reprint of the recent interview with our friends over at PNY to discuss a variety of topics affecting data scientists conducting work on big data problem domains including how “Big Data” is becoming increasingly accessible with big clusters with disk-based databases, small clusters with in-memory data, single systems with in-CPU-memory data, and single systems with in-GPU-memory data. Answering our inquiries were: Bojan Tunguz, Senior System Software Engineer, NVIDIA and Carl Flygare, NVIDIA Quadro Product Marketing Manager, PNY.

The Future Starts Now – Achieving Successful Operation of ML & AI-Driven Applications

Operationalizing AI and ML has become an unavoidable need in business, as various industries heavily rely on large volumes of real-time data as input to automated decision-making processes to yield the best results. Use cases in the data science field have shown that ML models and AI have few tangible business benefits until they are operationalized. In this e-book, our friends over at MemSQL show us how to successfully deploy model-driven applications into production.

Building Powerful Enterprise AI Infrastructure: How to Design Enduring Infrastructure for AI

Our friends over at cloud-neutral colocation data center company Interxion have published a whitepaper titled, “Building Powerful Enterprise AI Infrastructure: How to design enduring infrastructure for AI,” which details the requirements of an ideal infrastructure environment when it comes to reaping the benefits of today’s growing volume of data and enabling AI at scale.

The Essential Guide: Machine Scheduling for AI Workloads on GPUs

This white paper by Run:AI (virtualization and acceleration layer for deep learning) addresses the challenges of expensive and limited compute resources and identifies solutions for optimization of resources, applying concepts from the world of virtualization, High-Performance Computing (HPC), and distributed computing to deep learning.

Obstacles to AI & Analytics Adoption in The Cloud

Trifacta launched a benchmark report in conjunction with Researchscape: Obstacles to AI & Analytics Adoption in the Cloud, to examine how data workers in the U.S. across industries are handling the increased move of data to the cloud, the time constraints endured when preparing data for analytics, artificial intelligence and machine learning initiatives, and the impact these obstacles have on the overall success of these projects.

Machine learning for all: the democratizing of a technology

In this short eBook, you’ll discover automated machine learning using H2O.ai. H2O.ai has dedicated itself to democratizing all aspects of AI, including machine
learning. H2O Driverless AI is a machine learning solution that automates AI for nontechnical
users. So-called “AutoML” solutions like H2O Driverless AI are rising in popularity for enterprises across a wide range of industries. With it, users can build robust, fast, and accurate machine learning solutions. It also includes visualization and interpretability features that explain the data modeling results in plain English, fostering further adoption and trust in AI.

Overcoming Obstacles to Machine Learning Adoption

This is a new Business Impact Brief from 451 Research sponsored by H2O.ai – “Overcoming Obstacles to Machine Learning Adoption.” After many fits and starts, the era of enterprise machine learning has finally arrived. According to 451 Research’s Voice of the Enterprise, AI and Machine Learning survey, 20% of enterprises have already deployed the technology and a further 33% plan to do so within one year.

Ethical AI: Five Guiding Pillars

Corporate responsibility is not a new mission, but it has become a more complicated one as machine learning assumes a larger role in how work is done. This 20 page white paper provides five actionable ways organizations can re-imagine business models around ethical AI, according to KPMG.

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.

AI in the Enterprise: Trends & Insights on Vendor Selection and Implementation

A new report was released by our friend over at Leverton, a data extraction startup recently acquired by MRI Solutions, titled “AI in the Enterprise: Trends & Insights on Vendor Selection and Implementation.” The report lends insight into the experiences and preferences of “leaders,” “lookers,” and “laggards” (defined below) when it comes to AI deployment.