The insurance organizations have a very special niche in the world of data. In sharp contrast to most other fields where data related to financial information or information about personal identity is processed individually, insurers are used to processing both...
Optimizing SaaS Customer Lifetime Value (CLTV) requires continuous collection and monitoring of customer behavior data. Traditional data science pipelines lack scalability due to manual feature engineering and misalignment between transactional and analytical...
AI agents are on the rise, and it’s quickly shifting from a buzz word to reality. Development of agentic systems is fast moving from research labs and demos to production systems that can plan, act and learn from user workflows. The large language models (LLMs) that...
Consumer health products are developed or formulated based on marketing trends, trending “hero” ingredients, supplier stock formulations, and/or formulation expert subjectivity, and sometimes research. While these ways have historically made some market...
A subtle selection bias in machine learning-based causal inference can flip your results — here’s how to spot and fix it Machine learning is great at predicting outcomes. But when we try to use it to estimate causal impact, especially in a two-stage setup,...
I spent months overengineering an AI chatbot. Then I threw most of it away and got better results in two weeks. This is an anecdote of what actually worked when I built production RAG systems at scale, and why the lessons surprised me. The Complexity Trap I was tasked...
Due to the recent advancements in artificial intelligence (AI) and machine learning (ML), organizations are increasing investments in information technology (IT) infrastructure that best supports automation enabled by AI and ML. While these automated systems can guide...
Most data and AI projects don’t fail because teams chose the wrong infrastructure or modeling approach. They fail because the project breaks down somewhere across the lifecycle, long before folks realize. By the time something reaches production (if it does), teams...
Most current workflow systems are non-dynamic (static) in nature. Once a workflow is defined (data ingestion → filtering → inference), it does not adapt when edge computing devices fail (e.g., GPUs overheat) or network conditions degrade. This article...
In the high-stakes world of IT infrastructure, the difference between a minor glitch and a major outage is often measured in minutes. Yet, for most organizations, the incident response lifecycle remains stuck in a manual era. As cloud environments grow...