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Building a Data Catalog: A Guide to Planning & Implementing

Building and implementing a data catalog can help your enterprises’ data community discover and use the best data and analytics resources for their projects. A data catalog can help businesses achieve faster results, and make better decisions. As for the next steps to address the importance of data catalogs in your business, Data.world covers that, as well, in a new report.

A ‘Pre-Flight Checklist’ for Machine Learning Training Data

Machine learning is often key to success for today’s institutions that rely heavily on data for success. But often, data science teams can have a difficult time convincing their organizations of the breadth and size of a training data challenge. A new report from Alegion walks through a checklist to review before helping your enterprise take the next step in machine learning.

Scaling Production AI

As AI models grow larger and more complex, it requires a server architecture that looks much like high performance computing (HPC), with workloads scaled across many servers and distributed processing across the server infrastructure. Barbara Murphy, VP of Marketing, WekaIO, explores how as AI production models grow larger and more intricate, server architecture gets more complex. Explore how tools like GPU clusters and more are moving the dial forward on AI. 

AI Critical Measures: Time to Value and Insights

AI is a game changer for industries today but achieving AI success contains two critical factors to consider — time to value and time to insights.  Time to value is the metric that looks at the time it takes to realize the value of a product, solution or offering. Time to insight is a key measure for how long it takes to gain value from use of the product, solution or offering.

Are B2B Marketers Leaving Money on the Table by Ignoring B2C Data?

In this contributed article, Collin Dayley, Senior Vice President Sales and Strategic Partnerships at Versium, suggests that typically, B2B data focuses on role, and firmographic information. While B2C data can reveal information providing clues to the emotional reasons and process your customers use when making buying decisions. By combining both B2C and B2B data, marketers can develop more relevant content and experiences that meet individual buyer needs. This is proven to increase the ability to contact and engage B2B buyers.

AnotherBrain Reveals a New Generation of Artificial Intelligence

AnotherBrain is bringing its Organic AI™ technology that can transform every sensor into a smart sensor for use in industrial automation, automotive and IoT markets. AnotherBrain’s technology is explainable by design, uses low energy, and low data in order to make the deployment of artificial intelligence (AI) in factory and logistics more practical and effective.

AI Goes Mainstream

According to a recent Gartner survey, Artificial intelligence (AI) learning has moved from a specialized field into mainstream business use with 37 percent of respondents reporting their enterprises either had deployed AI or would do so shortly. WekaIO’s Barbara Murphy explores the path of artificial intelligence from the fringe to mainstream business practices. Find out what is driving AI growth and adoption.

How to Get to the Data-Enabled Data Center

Despite their many promising benefits, advancements in Artificial Intelligence (AI) and Deep Learning (DL) are creating some of the most challenging workloads in modern computing history and put significant strain on the underlying I/O, storage, compute and network. An AI-enabled data center must be able to concurrently and efficiently service the entire spectrum of activities involved in the AI and DL process, including data ingest, training and inference.

Why Providers Should Centralize Analytics

In this special guest feature, Hossein Fakhrai-Rad, President & Chief Scientific Officer at BaseHealth, discusses how In the future, the insights derived from centralized analytics delivery models are likely to help hospitals improve quality, lower costs, identify at-risk populations and better understand performance. For that to happen, however, hospitals and health systems must first overcome the fragmented, decentralized approach to analytics that prevents them from realizing the full value of their analytics investments.

How AI/ML Help Secure the US Power Grid Infrastructure

In this contributed article, William Ellis, discusses the U.S. power grid infrastructure and how artificial intelligence and machine learning can be leveraged to help secure the power grid, its infrastructure, and customers nationwide. During a time of widespread service disruptions, decisions will have to be made in terms of how to route resources. Smart systems can help to monitor the situation and provide intelligent recommendations, but that data must be used effectively by all involved.