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insideBIGDATA Latest News – 8/10/2022

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.

MLOps | Is the Enterprise Repeating the Same DIY Mistakes?

In this contributed article, Aaron Friedman, VP of Operations at Wallaroo.ai, discusses why hiring data scientists isn’t the answer to unlocking ML value (especially at a time when finding qualified candidates is harder than ever).

“Above the Trend Line” – Your Industry Rumor Central for 8/8/2022

Above the Trend Line: your industry rumor central is a recurring feature of insideBIGDATA. In this column, we present a variety of short time-critical news items grouped by category such as M&A activity, people movements, funding news, financial results, industry alignments, customer wins, rumors and general scuttlebutt floating around the big data, data science and machine learning industries including behind-the-scenes anecdotes and curious buzz.

Doctors Find Artificial Intelligence is the Best Prescription for Expert Assistance and Patient Care

In this special guest feature, Amir Atai, Ph.D. is Co-Founder and CEO of Sway AI, examines how AI is changing healthcare by improving the efficiency and quality of care on many fronts, starting with administration. The article also does a deep dive into the challenges of using AI in healthcare and how no-code AI and machine learning are automating mundane tasks and adding new diagnostic and treatment solutions to make doctors and nurses more efficient.

Artificial Intelligence – What’s in a Name?

As the tech industry hype cycle continues to churn my in-box every day, I find myself reflecting on the meme du jour of “artificial intelligence.” My initial reaction to over-hyped terms is to resist giving them more credence than they may deserve. “AI” associated with just about everything falls in line with my reticence. Somehow all new products and/or services are related to “AI”: AI-based, AI-powered, AI-fueled, AI-motivated (oh come on!).

Heard on the Street – 8/1/2022

Welcome to insideBIGDATA’s “Heard on the Street” round-up column! In this regular feature, we highlight thought-leadership commentaries from members of the big data ecosystem. Each edition covers the trends of the day with compelling perspectives that can provide important insights to give you a competitive advantage in the marketplace.

Fiddler Announces Giga-Scale Model Performance Management with Deeper Understanding of Unstructured Models and Fine Discoverability to Launch New AI Initiatives

Fiddler, a pioneer in Model Performance Management (MPM), announced major improvements to its MPM platform, including model ingestion at giga-scale, natural language processing (NLP) and computer vision (CV) monitoring, class imbalance, and an intuitive and streamlined user experience. With these new features, the Fiddler MPM platform is delivering a deeper understanding of unstructured model behavior and performance, and enhanced scalability, discoverability of rare and nuanced model drifts, and ease of use.

Research Highlights: Why Do Tree-based Models Still Outperform Deep Learning on Tabular Data?

In this regular column we take a look at highlights for breaking research topics of the day in the areas of big data, data science, machine learning, AI and deep learning. For data scientists, it’s important to keep connected with the research arm of the field in order to understand where the technology is headed. Enjoy!

A “Glass Box” Approach to Responsible Machine Learning 

In this contributed article, editorial consultant Jelani Harper suggests that machine learning doesn’t always have to be an abstruse technology. The multi-parameter and hyper-parameter methodology of complex deep neural networks, for example, is only one type of this cognitive computing manifestation. There are other machine learning varieties (and even some involving deep neural networks) in which the results of models, how they were determined, and which intricacies influenced them, are much more transparent.

Run:ai Releases Advanced Model Serving Functionality to Help Organizations Simplify AI Deployment 

Run:ai, a leader in compute orchestration for AI workloads, announced new features of its Atlas Platform, including two-step model deployment — which makes it easier and faster to get machine learning models into production. The company also announced a new integration with NVIDIA Triton Inference Server. These capabilities are particularly focused on supporting organizations in […]