When you build a machine learning system, the model gets all the glory. But for that model to do its job, it needs data—and that data needs to move. It flows from a database to a feature engineering service, from a prediction service to a user interf...
When machine learning comes up in conversation, the focus almost always turns to models: the algorithms, the architectures, the breakthroughs. But models don’t learn in a vacuum. Their intelligence depends entirely on the data they’re trained on. In ...
In the first article of this series, we looked at why the machine learning algorithm itself is only about 10% of the solution, and why a production-ready ML system is really about the bigger ecosystem that surrounds it. (find link to it here: https:/...
When we talk about machine learning, it's easy to get fixated on the algorithms. We think of neural networks, logistic regression, or XGBoost. But the truth is, the algorithm is just a small piece of a much larger, more complex puzzle. A production-r...
Part 1 of the "Building Trustworthy AI" Series Introduction Large Language Models (LLMs) are transforming industries, powering applications like support chatbots, content generation tools, and advanced data querying systems using Retrieval Augmented ...
APIs (Application Programming Interfaces) are the backbone of modern software ecosystems. They enable seamless communication between applications, powering everything from mobile apps to enterprise systems. However, as the demand for APIs grows, so d...
