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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.

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.”