Video Highlights: The 3 Steps of LLM Training with Lisa Cohen

In this video presentation, our good friend Jon Krohn, Co-Founder and Chief Data Scientist at the machine learning company Nebula, is joined by Lisa Cohen, Google’s Director of Data Science and Engineering, to discuss the capabilities of the cutting-edge Gemini Ultra LLM and how it stands toe-to-toe with GPT-4.

Navigating Cloud Migration: Choosing right Database for Cloud Migration of Your Data

In this contributed article, technical leader Kamala Manju Kesavan believes it is essential to periodically reassess your database strategy to ensure that it continues to meet your organization’s evolving requirements. If migrating to another database solution is deemed necessary, approach the process methodically, leveraging best practices and stakeholder collaboration to maximize success and drive business value.

Heard on the Street – 3/7/2024

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.

AI Washing: Unmasking the Illusion

In this contributed article, Maxime Vermeir, Senior Director of AI Strategy at ABBYY, discusses the term “AI Washing” which has emerged as a modern-day mirage, beguiling businesses into pouring resources into AI solutions that, unfortunately, fall short of solving real-world problems. The market is rife with lofty declarations of “innovation” and “Generative AI” utilization, yet they seldom offer a lucid narrative on tangible business outcomes.

Overcoming the Technical and Design Hurdles for Proactive AI Systems

In this contributed article, George Davis, founder and CEO of Frame AI, howlights how we find ourselves at an early, crucial stage in the AI R&D lifecycle. Excitement over AI’s potential is dragging it into commercial development well before reliable engineering practices have been established. Architectural patterns like RAG are essential in moving from theoretical models to deployable solutions.

Using Clinician Big Data to Alleviate a Struggling Workforce

In this contributed article, Charlie Lougheed, CEO and co-founder of Axuall, explains why healthcare needs to adjust its thinking and in what ways clinician big data can make impactful changes throughout the healthcare industry, from credentialing to attrition.

Video Highlights: A Code-Specialized LLM Will Realize AGI — with Jason Warner

In this video presentation, our good friend Jon Krohn, Co-Founder and Chief Data Scientist at the machine learning company Nebula, is joined by poolside co-founder and CEO Jason Warner who sheds light on how code-specialized LLMs could vastly outperform generalized counterparts like GPT-4.

Heard on the Street – 2/29/2024

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.

NEW RESEARCH: Growing Database Complexity Will Fuel Significant Skills Gaps in 2024

Increasing complexity, the rapid adoption of emerging technologies and a growing skills gap are the biggest concerns facing IT leaders in 2024, according to The State of the Database Landscape, a major new survey from end-to-end Database DevOps provider Redgate. 

AI Integration in the Service Industry: Navigating Challenges and Leveraging Human Expertise 

In this contributed article, Edwin Pahk, SVP of Presales & Customer Success at Aquant, discusses how AI is revolutionizing industries, especially in the service sector involving complex equipment maintenance, by promising efficiency and innovation. However, its integration faces challenges like adapting infrastructure, cultural shifts, and the need for evolved employee skills, requiring a human-in-the-loop approach and transforming AI vendors from mere providers to strategic advisors, while democratizing AI skills across the workforce.