Data Science 101: The Data Science Venn Diagram

Welcome to insideBIGDATA’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

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.

“Above the Trend Line” – Your Industry Rumor Central for 4/26/2023

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.

The ML Observability Checklist

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

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

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

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.

Scale Zeitgeist: AI Readiness Report

Our friends over at Scale are excited to introduce the 2nd edition of Scale Zeitgeist: AI Readiness Report! The company surveyed more than 1,600 executives and ML practitioners to uncover what’s working, what’s not, and the best practices for organizations to deploy AI for real business impact.

The Elements of Hyperautomation and How It Fits into a Modern IT Stack

In this special guest feature, Robert Duffner, digital portfolio leader for NTT DATA’s Enterprise Application Services, outlines what hyperautomation is and how to determine if it is a fit for your organization.

How to Scale AI Infrastructure Effectively, Without Sacrificing Cost Efficiency

Read this white paper, “Investing in AI Infrastructure,” from our friends over at IDC and Supermicro that explores how to scale AI infrastructure effectively, without sacrificing cost efficiency.