Machine Learning

Introduction Artificial Intelligence (AI) and Machine Learning (ML) are no longer just buzzwords floating around tech conferences and boardroom discussions. They have become integral components of modern enterprise architectures, driving innovation and operational efficiencies. Yet, as AI/ML technologies mature, so does the complexity of their deployment, particularly in cloud

Introduction Artificial Intelligence (AI) and Machine Learning (ML) continue to redefine how businesses operate, offering unprecedented opportunities for innovation and efficiency. The cloud has become the quintessential platform for deploying AI/ML solutions due to its scalability, flexibility, and cost-effectiveness. This guide provides a detailed examination of AI/ML infrastructure

Introduction Artificial Intelligence (AI) and Machine Learning (ML) are no longer just buzzwords floating around tech conferences and boardroom discussions. They have become integral components of modern enterprise architectures, driving innovation and operational efficiencies. Yet, as AI/ML technologies mature, so does the complexity of their deployment, particularly in cloud

2025 was supposed to be the year AI changed everything. The promises were bold: AI agents would "materially change the output of companies," scientific breakthroughs would accelerate, and the white-collar workforce would be transformed. Instead, we got something more interesting—and more useful—than the revolution we were

The AI industry just hit a counterintuitive inflection point that should concern every CTO deploying large language models in production: the more sophisticated our reasoning models become, the more frequently they hallucinate. This isn't speculation or vendor FUD. It's measurable, documented, and admitted by the companies

After spending the last several years architecting AI workloads across AWS, Azure, and GCP, I've come to a sobering conclusion: our current cloud infrastructure was built for yesterday's applications, not tomorrow's AI revolution. While cloud providers rush to slap "AI-powered" labels on

A deep dive into the data engineering, automation, and machine learning powering next-generation marketing measurement platforms—covering scalable data workflows, experiment automation, and the technical challenges of building a true system of record for marketing analytics.

In today's rapidly evolving AI landscape, organizations are increasingly faced with the challenge of building robust, scalable infrastructure to support their machine learning initiatives. While cloud-managed ML services offer convenience, many enterprises require more control, flexibility, and cost optimization than these services can provide. This is where Kubernetes