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Combining the Benefits of Commercial & Open Analytics

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.

Explore How to Detect and Address Machine Learning, AI Bias

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. 

Interview: Ashutosh Garg, CEO at Eightfold.ai

I recently caught up with Ashutosh Garg, CEO at Eightfold.ai to discuss how he and his team have deployed AI and machine learning to help with the needs of the talent management industry. For example, the company uses Deep Learning to take the candidate data available inside the enterprise and combine it with publicly available data to create a current, rich and deep profile of candidates.

Analytics Development Life Cycle: Pangea is Panacea

Sai Prakash from HCL America gave this talk at the Stanford HPC Conference. “In this short talk we shall present an analytics workbench perspective (Pangea) that brings entire ADLC under single umbrella thus enabling collaboration, shrinking overall cycle time, easing model deployment efforts and allowing model monitoring. Actionable insights and visualizations are facilitated though service integration interfaces.”

The Next Generation of Managing Enterprise Data: Cloud Optimized

The inherent elasticity and agility of a cloud-based data platform provides a major operational and financial advantage. This is the final article in a three-part series from Podium. This post focuses on the growing importance of being cloud optimized for managing enterprise data.

The Next Generation of Managing Enterprise Data: Data Conductor

With capabilities to act as what we call a “data conductor”, businesses are free to work with any data at anytime, anywhere to best serve business goals, unlocking the constraints of any data management environment. This sponsored post from Chris Ortega, Senior Director, Customer Success, at Podium Data, is the second in a three-part series focused on the need for increased data agility.

The Next Generation of Managing Enterprise Data: Intelligent Data Identification

In order to thrive in today’s market, businesses must demand more from their data – more insights, more agility and more flexibility. Intelligent Data Identification capabilities go beyond the metadata repository to leverage 80+ profiling statistics for core data insights and delivering on key value-add business use cases.

Dr. Eng Lim Goh on New Trends in Big Data and Deep Learning for Artificial Intelligence

In this video from SC16, Dr. Eng Lim Goh from HPE/SGI discusses new trends in HPC Energy Efficiency and Deep Learning for Artificial Intelligence. “Recently acquired by Hewlett Packard Enterprise, SGI is a trusted leader in technical computing with a focus on helping customers solve their most demanding business and technology challenges.”

Interview: Adam Compain, CEO of ClearMetal

I recently caught up with Adam Compain, CEO of ClearMetal, to discuss how AI already is being used in the logistics industry to decipher the global supply chain’s many big data complexities.

Teradata Showcases Presto at 2016 Hadoop Summit

“Presto is a perfect fit with the Teradata Unified Data Architecture, an integrated analytical ecosystem for our enterprise customers. Presto enables companies to leverage standard ANSI SQL to execute interactive queries against Hadoop data. With Presto, utilizing Teradata’s Query Grid connector for Presto, customers can execute queries that originate in Teradata Integrated Data Warehouse that join data within the IDW and Hadoop leveraging Presto.”