Ruchi Yadav

Every organization sits on a mountain of text they cannot read. Customer emails accumulating in support queues. Product reviews flowing in by the thousands. Legal contracts requiring clause-by-clause analysis. Employee survey responses that nobody has time to categorize. Medical notes from thousands of patient visits. Social media mentions that move

There is a persistent assumption in machine learning circles that production inference requires a persistent server. A running container. An always-on endpoint with a GPU idling in the background, waiting for the next request. This assumption is expensive, and for a large class of real-world ML applications, it is also

AWS Machine Learning | Platform Deep Dive There is a ritual that every machine learning engineer knows intimately. You open a fresh server instance. You spend the first two days not doing any machine learning at all, just fighting CUDA drivers, dependency conflicts, broken Docker images, and networking policies that nobody

Retrieval-Augmented Generation has become one of the most practical ways to make large language models useful in real-world applications. While demos often show impressive results, production-grade RAG systems require careful architectural choices to ensure reliability, relevance, and trust. A strong RAG system starts with understanding the nature of the data being

Here is a frustrating truth that the machine learning industry took years to admit publicly: most ML models never make it to production. Surveys put the number at somewhere between 70 and 90 percent of models trained in enterprise data science teams that never get deployed. They sit in notebooks,

Most people have interacted with computer vision without realizing it. The app that scans your face to unlock your phone. The checkout camera that flags shoplifted items. The platform that blurs your background on a video call. The system that stops your child from seeing inappropriate content online. Computer vision