Aaron Erickson at QCon AI NYC 2025 emphasized treating agentic AI as an engineering challenge, focusing on reliability ...

If we want to avoid making AI agents a huge new attack surface, we’ve got to treat agent memory the way we treat databases: ...

Learn key insights, risks, and best practices before migrating your app to Ruby on Rails for a smooth, secure, and scalable ...

AIM Media House brings the Data Engineering Summit 2026 to Bengaluru, uniting engineers and leaders to explore the frameworks ...

No single team can make AI explainable alone. Product teams define goals and clarify which decisions matter most. Data engineers design the pipelines and lineage that make inputs traceable. Scientists ...

Innovation fails when every pilot starts from scratch; the chassis approach shows how to move fast with vendors while keeping ...

In today’s digital-first world, your website is more than just an online presence-it’s your brand’s first impression, sales engine, and credibility booster rolled into one.

Scalable data validation reshaping FinTech as Sai Kishore Chintakindhi drives automation, ML accuracy and real-time trust.

In today’s data-driven economy, agility and intelligence are no longer optional—they are the foundation of competitive ...

Overview:  Defining who decides, what they decide, how fast it must happen, and how much risk is allowed shapes everything ...

1. Risk: AI Monoculture (Shared Blind Spots). This is the most critical and overlooked systemic vulnerability. Building your ...