Kovendo

When we say ML System Design, we’re talking about more than just training and deploying a model. It's the complete process of conceiving, engineering, and operating a system that leverages machine learning to deliver real-world value. In other words, machine learning system design is about turning models into functional, reliable components that serve users under real-world constraints. According to a report by Algorithmia, more than 55% of organizations take over a month to deploy an ML model , and over 40% of models never make it into production . Moreover, once deployed, ML models can degrade by as much as 10–20% in performance over six months if not properly monitored and maintained. From data ingestion to monitoring deployed models, every step matters. This blog walks you through the ML life cycle, ML model lifecycle, ML system architecture, approaches used in industry, how to measure success, and how to decide if outcomes are correct.   2. Breaking Down the ML Life Cyc...

In the age of digital information, the way we consume content has drastically changed. With just a few clicks, we are constantly surrounded by content that reflects our beliefs, interests, and preferences. While this sounds ideal, it often leads us into what experts call filter bubbles and echo chambers . A few years back  study by the Reuters Institute found that 28% of people worldwide actively avoid news that contradicts their views, highlighting the growing influence of these phenomena. Though the terms are often used interchangeably, they differ significantly and have a profound impact on our understanding of the world. This blog delves deep into these concepts, exploring their causes, consequences, and ways to break free. What are Filter Bubbles? Filter bubbles refer to the algorithmically-created digital environments where individuals are exposed primarily to information that aligns with their previous online behavior. This concept was introduced by Eli Pariser in his fi...