Ask any AI leader how their last project went and you’ll likely hear about accuracy. F1 scores. AUC. The model “performed well.” Ask them how it’s doing in production, and things get quieter. The truth is uncomfortable but necessary:Most AI doesn’t fail in training. It fails in deployment. Accuracy Is Not a Success Metric In […]
Most AI Fails in Deployment Read More »
There’s a disconnect we don’t talk about enough.Data systems have gone real-time.Enterprise planning hasn’t. While Kafka pipes millions of events per second and Flink runs stateful computations in motion, most enterprises are still operating on a roadmap that looks like a spreadsheet and moves like a barge. This isn’t just a pace mismatch—it’s a mindset
Real-Time Data Is Real—Your Enterprise Roadmap Isn’t Read More »
Why real-time systems aren’t luxury infrastructure—they’re how smart businesses stay ahead. Let’s get one thing out of the way:Batch is fine—for laundry. Not for decisions. Most companies still move data the same way they moved it in 2005: extract, load, wait, analyze, repeat. It’s comfortable. It’s familiar. But it’s also a few hours—or days—behind what’s
Kafka Isn’t Just a Queue. And Flink Isn’t Just a Buzzword. Read More »
In today’s digital world, real-time data streaming is no longer a luxury—it’s a necessity. Businesses and AI-driven applications thrive on instant insights, requiring robust data pipelines that can process and react to events as they happen. Apache Kafka and Apache Flink form the backbone of modern real-time data processing, enabling seamless data flow, low-latency analytics,
As we have explored in the previous parts of this series, high-quality and real-time data are essential for AI and ML applications. Now, let’s take a deeper look into how to implement real-time data products effectively using Apache Kafka and Apache Flink. This part focuses on two crucial features of Flink that enable reliable and
Implementing Real-Time Data Products with Apache Kafka and Apache Flink (Part 3) Read More »
I still remember the days in the early 2010s, when the term „big data“ was widely discussed in the tech industry. Companies were encouraged to collect as much data as possible, seeing it as a key resource (or the new oil as we called it those days) for the digital economy. However, focusing only on
What Are Data Products and Why Do They Matter for AI and ML? (Part 1) Read More »