Tag: AI
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Building AI Factories: How Hong Kong Companies Can Create Scalable GenAI Infrastructure on a Budget
From Cost Centre to Competitive Engine: The “AI Factory” Mindset For Hong Kong’s dynamic businesses, the promise of Generative AI (GenAI) is tempered by a harsh reality: the perceived high cost and complexity of building a robust, scalable infrastructure. Many companies find themselves trapped in “Pilot Purgatory,” where impressive prototypes—like the autonomous agent systems we’ve…
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Escaping Pilot Purgatory 2.0: Strategies for Scaling Agentic Workflows in Production
From Static Models to Dynamic Workflows: A New Frontier of Stalled Progress In my 2024 article, “Pilot Purgatory in Machine Learning,” I explored the frustrating gap where promising AI prototypes fail to deploy. Two years later, we face a more complex challenge: Pilot Purgatory 2.0. This isn’t about deploying a single model anymore—it’s about scaling dynamic…
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Agentic AI in 2026: From Hype to Real-World Deployment in Asian Enterprises
Introduction: Emerging from “Pilot Purgatory” Just two years ago, in my 2024 post on “Pilot Purgatory in Machine Learning,” I discussed the frustrating gap between promising prototypes and deployed production systems. Today, as we examine the state of Agentic AI in 2026, we witness a remarkable transformation. The landscape has evolved from isolated experiments with tools…
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Pilot Purgatory in Machine Learning: Why Most Models Excel in Prototyping but Fail to Deploy in Production
The POC-to-Production Gap: A Persistent Challenge in Applied ML As data scientists, we’ve all encountered the frustrating reality of “pilot purgatory”—where promising proof-of-concept (POC) models deliver impressive offline performance but never make it to production. Industry reports highlight the scale of this issue: estimates suggest that 70-80% of ML projects fail to reach production deployment,…
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AI Agents & Autonomous Systems: How AI Agents Like AutoGPT Are Evolving
In the rapidly advancing world of artificial intelligence, a new frontier is emerging—AI agents and autonomous systems. These aren’t just models that respond to prompts; they are self-directed digital entities that think, plan, and act toward achieving goals with minimal human intervention. At the center of this movement are AI agents like AutoGPT, BabyAGI,…
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AI Canvas to Work out AI
In this article, I would like to share the simple decision-making tool named AI Canvas and being used in MBA graduates at the university of Toronto’s Rotman School of Management, by professor Ajay Agrawal, University of Toronto. Before going into further details, it is vital to declare that AI / Data Science / Prediction…
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Hardware Investment on Data Science in 2019/20
In this article, I would like to share some technical staff rather than high level data science topic for managers. There are more and more organizations investing in Machine Learning (mostly TensorFlow) including the installation and configuration of new servers and GPU for the intensive computations. Different hardware or cloud service will be discussed. Development…
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Don’t abuse the term “AI” – Artificial Intelligence
There are lots of people introducing themselves as “AI experts” since 2017. However, I am not sure how many of them are understanding the difference between Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL). Most of the self-claimed AI experts are doing some ML or DL tasks in a particular area like Object Recognition (image…