StreamSets, a Software AG company, unveiled two reports highlighting the need for modern data integration: “Creating Order from Chaos: Governance in the Data Wild West,” and “Lifting the Lid on the Hidden Data Integration Problem.” This new research shows a widespread lack of data processes and policies among organizations, rising demand for data, and why many organizations fail to use the data at their disposal.
The 4 Ingredients for Accurate Supply Chain Data Synchronization
April 29, 2023 by
In this contributed article, Randy Mercer, VP of Global Products for 1WorldSync, believes that as a brand’s supply chain data responsibility evolves, so do the possibilities for content to provide value. By implementing any of the solutions itemized in the article, brands create a recipe for successfully achieving accurate, seamless supply chain data sharing.
If Big Data Is the Immovable Object, Enterprise Search Is the Unstoppable Force
April 28, 2023 by
In this special guest feature, Elizabeth, Director of Sales at dtSearch Corp., asks ‘How do you approach Big Data?’ You could try to organize the heck out of it if you have all of the time in the world and your data isn’t constantly changing. Or you could kick back and let enterprise search provide immediate access. If Big Data is the immoveable object, enterprise search is the unstoppable force.
Data Science 101: The Data Science Venn Diagram
April 28, 2023 by
Welcome to insideAI News’s Data Science 101 channel bringing you perspectives for the topics of the day in data science, machine learning, AI and deep learning. Many of the video presentations come from my lectures for my Introduction to Data Science class I teach at UCLA Extension. In today’s slide-based video presentation I discuss The Data Science Venn Diagram, a subject-by-subject overview of the constituent parts of the discipline of data science.
Heard on the Street – 4/27/2023
April 27, 2023 by
Welcome to insideAI News’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.
“Above the Trend Line” – Your Industry Rumor Central for 4/26/2023
April 26, 2023 by
Above the Trend Line: your industry rumor central is a recurring feature of insideAI News. 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.
The ML Observability Checklist
April 25, 2023 by
This checklist from our friends over at Arize covers the essential elements to consider when evaluating an ML observability platform. Whether you’re readying an RFP or assessing individual platforms, this buyer’s guide can help with product and technical requirements to consider across a number of areas discussed in this useful resource. The machine learning infrastructure ecosystem is confusing, crowded and complex. With so many companies making competing claims and so much at stake when model performance regresses in production, it can be easy to feel overwhelmed. However, the need for better ML observability tools to monitor, troubleshoot, and explain model decisions is clear.
Centralized Data, Decentralized Consumption
April 25, 2023 by
In this special guest feature, DeVaris Brown, CEO and co-founder of Meroxa, details some best practices implemented to solve data-driven decision-making problems themed around Centralized Data, Decentralized Consumption (CDDC). We’ll start by looking at the problems, why the current solutions fail, what CDDC looks like in practice, and finally, how it can solve many of our foundational data problems.
Power to the Data Report: Introduction to Neural Magic
April 24, 2023 by
Neural Magic is a startup company that focuses on developing technology that enables deep learning models to run on commodity CPUs rather than specialized hardware like GPUs. The company was founded in 2018 by Alexander Matveev, a former researcher at MIT, and Nir Shavit, a professor of computer science at MIT. They raised a total of $50 million in funding to date over 3 rounds, from investors such as Comcast Ventures, NEA, Andreessen Horowitz, Pillar VC, and Amdocs.
16-Year-Old Data Scientist Creates R Shiny App to Champion Gender Equality in Sports Media Coverage of NCAA Women’s Basketball
April 23, 2023 by
Nathaniel Yellin, a 16-year-old student, has concluded a new study that reveals the significant gender bias in the sports media coverage of female athletes and, in particular, college basketball players. Yellin has pursued his passions for sports, data science and inspiring change through the creation of an organization and interactive R Shiny application SIDELINED.
