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3 Non-Obvious Keys to Being AI-Ready

Data scientists know what they are doing, and most organizations have no cause to worry about the soundness of their machine learning (ML) algorithms. Where AI readiness typically lags is in other parts of the process. In most organizations today, the process of building, deploying and maintaining AI systems bears no resemblance to traditional IT. Alegion explores three key strategies your business can employ to be AI-ready.

Distributed GPU Performance for Deep Learning Training

If there is a time deadline by which training must be completed, or if it simply takes too long to complete training, distributing the workload across many GPUs can be used to reduce training time.  This flexibility allows GPU resources to be maximally utilized and provides high ROI since time to results can be minimized. HPE highlights recent research that explores the performance of GPUs in a scale-out and scale-up scenarios for deep learning training. 

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.

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.

New Guide Offers Databricks Unified Analytics Platform Machine Learning Use Cases

The fields of machine learning and deep learning are on the brink of unprecedented breakthroughs across a variety of verticals. And according to a new report from Databricks, “data is the new fuel,” for these market advancements. Download the new white paper today, “Four Real-Life Machine Learning Use Cases,” to explore Databricks Unified Analytics Platform use cases in the advertising, loan servicing, media industries and more.

Using Unified Analytics & Big Data as Path to AI Success

How can modern enterprises unlock the potential of AI to change their business? Today’s businesses and enterprises are increasingly focused on big data that can help drive innovation and transformation through the potential of artificial intelligence. According to a survey and research report commissioned with IDG’s CIO, nearly 90 percent of enterprises are investing in data and AI technology. Download the new report, “Unified Analytics for Dummies,” that explores the steps to AI success in today’s market.