Production patterns for AI agents, RAG pipelines, data infrastructure, and MLOps. No theory-only posts — every article comes from a real deployment.
A practical guide to data science in banking, covering analytics and AI use cases such as fraud detection, credit risk, AML, churn prediction, customer intelligence, and operations.
A 2026-safe look at what deep learning can and cannot do for Bitcoin forecasting, with a more realistic framing for model design and evaluation.
A 2026 refresh of the old 2018 trend list, focused on which themes actually endured and what still matters for AI, data, and platform teams.
A practical BI tools comparison covering six widely used business intelligence, dashboarding, and data visualization platforms, with guidance on fit, tradeoffs, and operating model.
A modern guide to Scala libraries for data science, streaming, analytics, and JVM-native machine learning that still matter in real production systems.
A modern guide to the Python libraries for data science that still matter most across analytics, machine learning, visualization, and production data work.
A practical guide to the command-line tools that remain useful for data scientists, analysts, and data engineers working with files, logs, remote systems, and quick inspection tasks.
A retrospective look at which early big data and data science trends became durable and which ideas evolved into today’s operating model.
A practical overview of how open data and smart-city systems can improve urban operations, public services, and decision-making.
A practical guide to graph database use cases and applications, including knowledge graphs, fraud detection, AML, customer 360, cybersecurity, recommendations, and supply chain visibility.
A practical introduction to MongoDB, document databases, and the kinds of workloads where MongoDB is a strong fit.
A practical guide to installing VirtualBox on Ubuntu, running local VMs, and deciding when a full Ubuntu virtual machine still makes sense.