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TOP 10 insideBIGDATA Articles for May 2021

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.

insideBIGDATA Latest News – 6/1/2021

In this regular column, we’ll bring you all the latest industry news centered around our main topics of focus: big data, data science, machine learning, AI, and deep learning. Our industry is constantly accelerating with new products and services being announced everyday. Fortunately, we’re in close touch with vendors from this vast ecosystem, so we’re in a unique position to inform you about all that’s new and exciting. Our massive industry database is growing all the time so stay tuned for the latest news items describing technology that may make you and your organization more competitive.

AIOps: The Iron Man Suit for IT Leaders

In this special guest feature, Josh Atwell, Senior Technology Advocate at Splunk, believes that by leaning into their data and embracing AIOps, IT teams are improving the customer experience. This begs the question: How can IT leaders leverage AIOps to build an informed plan that keeps up with the ever-changing demands of the business and customers?

Video Highlights: Challenges of Operationalizing ML

In the panel discussion below, the focus is on the main challenges of building and deploying ML applications. The discussion includes common pitfalls, development best practices, and the latest trends in tooling to effectively operationalize. The presentation comes from apply(): The ML Data Engineering Conference sponsored by Tecton.

APIs: The Real ML Pipeline Everyone Should Be Talking About

In this special guest feature, Rob Dickinson, CTO, Resurface Labs, suggests that to achieve greater success with AI/ML models, through accurate business understanding, clear data understanding, and high data quality, today’s API-first organizations must shift towards real-time data collection.

Understanding “Human Intent and Behavior” with Computer Vision

In this contributed article, editorial consultant Jelani Harper discusses how computer vision is one of the most eminent forms of statistical Artificial Intelligence in use today. Comprised of varying facets of object detection, facial recognition, image classification, and other techniques, it supports a range of pressing use cases from contact-less shopping to video surveillance.

Quantum Machine Learning – An Introduction to QGANs

In this contributed article, data scientists from Sigmoid discuss quantum machine learning and provide an introduction to QGANs. Quantum GANs which use a quantum generator or discriminator or both is an algorithm of similar architecture developed to run on Quantum systems. The quantum advantage of various algorithms is impeded by the assumption that data can be loaded to quantum states. However this can be achieved for specific but not generic data.

Walmart Innovates at the Retail Edge with AI

Through the combination of world-class software from Malong Technologies and a powerful processing platform Dell Technologies and NVIDIA, Walmart is able to deliver groundbreaking AI acceleration at the store edge.

Best of for AI, Machine Learning, and Deep Learning – April 2021

In this recurring monthly feature, we will filter all the recent research papers appearing in the preprint server for subjects relating to AI, machine learning and deep learning – from disciplines including statistics, mathematics and computer science – and provide you with a useful “best of” list for the month.

Democratizing Machine Learning to Empower the Modern Business

In this special guest feature, Adam Carrigan, Co-founder and COO of MindsDB, discusses how the democratization of machine learning makes predictive analytics accessible to companies of any size. While machine learning makes a transformative impact on the business world, it seems larger enterprises are the only ones reaping the benefits. So what about smaller and medium-sized businesses? Are they forced to remain on the sidelines of the AI revolution?