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TOP 10 insideBIGDATA Articles for July 2019

In this continuing regular feature, we give all our valued readers a monthly heads-up for the top 10 most viewed articles appearing on insideBIGDATA. Over the past several months, we’ve heard from many of our followers that this feature will enable them to catch up with important news and features flowing across our many channels.

Optimizing Fuel Pricing in a Convenience Retail Environment with AI and Machine Learning

In this special guest feature, Niels Skov, SVP, PDI Fuel Pricing Solutions, outlines how fuel pricing is a complex business for convenience retailers. Using advanced digital capabilities like AI and machine learning to get fuel pricing right can have a significant business impact far beyond an operator’s raw margins on gasoline or diesel.

The Future of AI Surveillance Around the World

In this contributed article, tech journalist Paul Bischoff discusses the increasing use of AI-driven surveillance around the world, and the dangers it creates. Realistically, the best way to protect yourself against intelligent surveillance systems is to stop them before they become a problem.

The AI Opportunity

The tremendous growth in compute power and explosion of data is leading every industry to seek AI-based solutions. In this Tech.Decoded video, “The AI Opportunity – Episode 1: The Compute Power Difference,” Vice President of Intel Architecture and AI expert Wei Li shares his views on the opportunities and challenges in AI for software developers, how Intel is supporting their efforts, and where we’re heading next.

What to Ask Yourself when Hiring a Data Scientist

In this special guest feature, Aria Haghighi, VP of Data Science at Amperity, discusses several important questions to ask yourself when hiring a data scientist. Hiring data scientists is hard. They’re hard to find since there are fewer trained than can meet demand, and it’s challenging to properly interview and vet them (especially the first in your organization).

Help! My Data Scientists Can’t Write (Production) Code!

In this contributed article, Nisha Talagala, Co-founder and CTO/VP of Engineering at ParallelM, takes a hard look at productionizing machine learning code and how integrating SDLC practices with MLOps (production ML) practices certifies that all code, ML or not, is managed, tracked and executed safely.

Fast-track Application Performance and Development with Intel® Performance Libraries

Intel continues its strident efforts to refine libraries optimized to yield the utmost performance from Intel® processors. The Intel® Performance Libraries provide a large collection of prebuilt and tested, performance-optimized functions to developers. By utilizing these libraries, developers can reduce the costs and time associated with software development and maintenance, and focus efforts on their own application code.

Infographic: The AI Economy

By 2030, 70% of companies worldwide will be using some form of AI tech. This infographic from our friends over at Noodle.ai outlines how AI will affect the global economy as it is integrated into more businesses. Sign up for the free insideBIGDATA newsletter.

What Happened to Hadoop? And Where Do We Go from Here?

Apache Hadoop emerged on the IT scene in 2006 with the promise to provide organizations with the capability to store an unprecedented volume of data using cheap, commodity hardware. Hadoop facilitated data lakes were accompanied by a number of independent open source compute engines – and on top of that, “open source” meant free! What could go wrong?

How to Adapt to New Technology and Gain a Holistic View of Your Customer

In this special guest feature, Daniel Herdean, CEO of Cognetik, provides four key tips that can help your organization adapt to the new influx of data and ensure you’re providing the best customer experience possible.