news – Data and AI Agents Done Right

AI Agents Survey Results Part I

The YouGotUs 2024 AI Agents in Production Survey highlights that 68% of companies have deployed AI agents, with only 32% achieving significant ROI. Key findings include integration and scalability as top challenges, alongside trends in automation and hybrid intelligence. Explore the detailed report. Part 1 of YouGot.us’ AI Agent study, conducted during the AI Agent in Production Event by Prosus and MLops.Community, covers the definition of AI Agents, users and cuse cases as well as agent adoption.

Christina Garcia

Announcing AI Agents in Production Study

Commercial AI agent solutions currently dominate enterprise adoption, although open-source frameworks are gaining traction for customization and cost control. Organizations are increasingly combining both approaches to address diverse needs. Industry-specific use cases and robust MLOps infrastructure remain top priorities, reflecting a market evolving toward specialized, flexible AI deployments. The YouGot.us AI Agents in Production study provides fresh insights into these trends, guiding businesses on how to navigate the evolving landscape.

Christina Garcia

Data Engineering for AI

Transformative outcomes in AI initiatives depend on the strength and scalability of data engineering frameworks. By enabling efficient orchestration, real-time data processing, and optimized workflows, modern data engineering lays the foundation for impactful AI solutions. This post examines the tools, methodologies, and strategies shaping the future of data engineering, with research-driven insights into overcoming challenges in scaling AI pipelines. It also explores how aligning data workflows with AI innovation drives measurable results, fostering organizational transformation and unlocking new opportunities.

Christina Garcia

AI in Production

Deploying large language models (LLMs) in production environments presents unique challenges and opportunities. This post delves into best practices for scaling, monitoring, and optimizing LLMs to ensure reliability and real-world impact. It explores the complexities of integrating LLMs into production workflows, from fine-tuning and deployment strategies to addressing latency and cost-efficiency. With research-driven insights, the post highlights how organizations can harness the transformative potential of LLMs to drive measurable outcomes and create sustained value.

Christina Garcia