Thoughts on AI, digital transformation, and technology leadership in Southeast Asia.
Batch analytics is no longer sufficient for competitive organizations. Here is how to architect real-time analytics pipelines that deliver insights when they matter most.
In the age of deep learning, feature engineering is considered by some to be outdated. They are wrong. For enterprise ML, feature engineering remains the highest-leverage activity.
The data architecture debate has evolved beyond data lakes and warehouses. Here is how to choose between data mesh, data lakehouse, and hybrid approaches for your enterprise.
You do not need a PhD to evaluate AI performance. Here is how business leaders can assess whether an AI model is actually good enough for production.
Choosing the right cloud platform for AI workloads is a decision that will shape your AI capability for years. Here is my comparative analysis based on enterprise deployments across all three platforms.
Running Kubernetes in a tutorial is easy. Running it at enterprise scale with real workloads, real security requirements, and real users is a completely different challenge.
Despite the cloud-first narrative, most enterprises need hybrid architectures. Data sovereignty, latency, cost, and legacy integration all demand a more nuanced approach.
Indonesia is at an inflection point. With 280 million people, a young workforce, and accelerating digital adoption, the country is poised to become Southeast Asia's AI leader.
AI is not just optimizing the media industry — it is fundamentally restructuring how content is created, distributed, and consumed. Here is what I learned leading technology at a major media company.