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Can Microservices Revolutionize Data Management in the Enterprise?

In this contributed article, Mike Duensing, Chief Technology Officer and Executive Vice President of Engineering at Skuid, explains why why enterprises may want to move to a microservice architecture to ensure they are effectively managing data employing features of AI and machine learning.

Blockchain Supercharged with AI: The Next Revolution?

In this contributed article, InWara Business Analyst Gregory S. Mathew, discusses the convergence of blockchain and AI and how recent developments in big data has created a conducive environment for the amalgamation of these technologies.

Intelligent Operations: How to Navigate the Journey from Manual to Automated

In this special guest feature, Tom Pohlmann, EVP of Customer Success at AHEAD, discusses how the road to fully functional Intelligent Operations is bumpy, no matter what stage of digital transformation companies are currently in. Based on his experience, he offers five stages of maturity that organizations typically fall into.

Field Report: KDD 2019

As a very long time member of the ACM and their SIGKDD group, I’d always wanted to attend a KDD conference (first one occurred in 1995). This year I received a gracious invitation to attend KDD2019 in Anchorage, Alaska, August 4-8. It satisfied two of my bucket list items: witnessing a KDD first-hand and also […]

The Most Common Missed Opportunities With Big Data

In this contributed article, technology writer and blogger Kayla Matthews offers insights into some of the most common missed opportunities with big data technologies.

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.

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.

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?