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insideBIGDATA Guide to Optimized Storage for AI and Deep Learning Workloads

Artificial Intelligence (AI) and Deep Learning (DL) represent some of the most demanding workloads in modern computing history as they present unique challenges to compute, storage and network resources. In this technology guide, insideBIGDATA Guide to Optimized Storage for AI and Deep Learning Workloads, we’ll see how traditional file storage technologies and protocols like NFS restrict AI workloads of data, thus reducing the performance of applications and impeding business innovation. A state-of-the-art AI-enabled data center should work to concurrently and efficiently service the entire spectrum of activities involved in DL workflows, including data ingest, data transformation, training, inference, and model evaluation.

“Above the Trend Line” – Your Industry Rumor Central for 9/16/2019

Above the Trend Line: your industry rumor central is a recurring feature of insideBIGDATA. In this column, we present a variety of short time-critical news items grouped by category such as M&A activity, people movements, funding news, financial results, industry alignments, customer wins, rumors and general scuttlebutt floating around the big data, data science and machine learning industries including behind-the-scenes anecdotes and curious buzz.

HPE Accelerates Artificial Intelligence Innovation with Enterprise-grade Solution for Managing Entire Machine Learning Lifecycle

Hewlett Packard Enterprise (HPE) announced a container-based software solution, HPE ML Ops, to support the entire machine learning model lifecycle for on-premises, public cloud and hybrid cloud environments. The new solution introduces a DevOps-like process to standardize machine learning workflows and accelerate AI deployments from months to days.

Lucidworks Fusion 5.0 Features Data Science Toolkit Integration and Microservices Architecture Orchestrated by Kubernetes

Lucidworks, a leader in AI-powered search, announced the release of Fusion 5.0, the latest version of the company’s flagship product. Lucidworks Fusion 5.0 lets customers rapidly develop and deploy AI-powered search and data discovery applications in a modern, Kubernetes-based containerized architecture. This update includes features that simplify implementation for data science teams and developers to create both a more personalized experience for digital commerce customers and also improve employee productivity and engagement in the digital workplace.

Data Integration Myth-Busting: Don’t Let These 4 Misconceptions Hold You Back

In this special guest feature, Jan Arendtsz, CEO of Celigo, suggests that business leaders and IT professionals know this tsunami of data is here, but many don’t yet have a comprehensive strategy to integrate it all, so they rely on stop-gap measures. Four data myths in particular hold companies back from creating an effective, long-term data integration strategy. Here’s a brief overview of each myth — and an explanation of why it’s leading businesses astray.

Tips for Using Kubernetes in Large Enterprises

In this contributed article, technology writer Gilad David Maayan provides a clear and concise overview of Kubernetes, the darling of the DevOps community. He then offers a number of useful tips for using Kubernetes on an enterprise scale.

Making Use of Under-Utilized Big Data in the Insurance Industry

In this special guest feature, Adam Bratt, CTO and Co-Founder of Indio Technologies, points out that the topic of big data in insurance has been an emerging hot topic over the last year. Insurance is directly related to most people as it deals directly with personal safety of health, life, and assets and for companies, it’s all of the company’s assets and how the business is run, the likelihood of success, etc. The implementation of big data analytics has been an important one. In the InsurTech industry, Big data analytics can be incredibly helpful for companies, however, privacy is a big concern for consumers.

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.

Interview: KDD2019 Co-General Chairs Ankur Teredesai & Vipin Kumar

During my trip to KDD2019 in August, I had the pleasure of sitting down to chat with the co-chairs of the conference, Ankur Teredesai and Vipin Kumar. In the interview that follows, we discuss the growth of the KDD conference over the years, and also it’s changing focus. KDD is touted as being “the premier interdisciplinary conference bringing together researchers and practitioners from data science, data mining, knowledge discovery, large-scale data analytics, and big data.”

When Data-Driven Meets Data Silos: Let the Fun Really Begin

In this special guest feature, Ed Thompson, CTO and co-founder at Matillion, believes that on balance, the systems that lead to having many data silos are a good thing; they indicate a business has the autonomy to choose the best systems in each department. This should make the business more efficient overall. However, the business needs data from all these systems.