AI systems are becoming more and more of the norm as machine and deep learning gain grown — especially within the data center and colocation markets. That said, Artificial Intelligence systems are only as good as their underlying mathematics and the data they are trained on. Download a new report from Alegion to further understand the bias behind machine learning and how to avoid four potential pitfalls.
New Guide Offers Databricks Unified Analytics Platform Machine Learning Use Cases
January 3, 2019 by
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
December 20, 2018 by
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.
Choosing the Right Data Catalog for Your Business
November 19, 2018 by
The decision to invest in and launch a data catalog is a big one for today’s businesses, but one that more and more companies are making in light of the continued growth and expansion of big data. In fact, the self-service data analytic journey often first begins with a data catalog. Unifi Software explores how to choose whether a data catalog is right for your business and key considerations when deciding on a solution.
Use Case Highlights Potential Value & Benefits of Data Cataloging
November 14, 2018 by
The value and benefits of a data catalog are often described as the ability for analysts to find the data they need quickly and efficiently. Data cataloging accelerates analysis by minimizing the time and effort that analysts spend finding and preparing data. A new report from Unifi Software offers a fresh perspective on data cataloging and offers an analysis use case that illustrates ROI for implementing a data catalog.
Combining the Benefits of Commercial & Open Analytics
September 11, 2018 by
A new e-book explores how organizations in many industries are using open source analytics and SAS, getting the most from both, and what role SAS plays throughout the analytics life cycle.
Improve Your Business with Modern Analytics Infrastructure
August 29, 2018 by
A new report from SAS explores how a modern analytics infrastructure and IT department can help you increase productivity, maintain the highest data quality through a standard data governance model, and make critical decisions faster and with more confidence.
Explore How to Detect and Address Machine Learning, AI Bias
August 23, 2018 by
Alegion is fully aware of the potential for machine learning bias because as they produce AI training data, the company is on the lookout for biases that can influence machine learning. A new white paper from Alegion, “Four Sources of Machine Learning Bias,” explores the four sources of AI bias, and how to mitigate these challenges for your AI systems.
AI and the Emerging Crisis of Trust
July 23, 2018 by
In this guest article, Doug Bordonaro, Chief Data Evangelist at ThoughtSpot, explores the role trust plays in adoption of AI in the home, our jobs and beyond.
Exploring the Convergence of AI, Data and HPC
January 23, 2018 by
The demand for performant and scalable AI solutions has stimulated a convergence of science, algorithm development, and affordable technologies to create a software ecosystem designed to support the data scientist. A special insideHPC report explores how HPC and the data driven AI communities are converging as they are arguably running the same types of data and compute intensive workloads on HPC hardware, be it on a leadership class supercomputer, small institutional cluster, or in the cloud.
