Here is how the story usually goes. You run a small AI pilot and it looks like it won’t incur a huge cost. The first month flies by, the model performs well, and the dashboard? That’s the best part. Only a modest compute bill. And then the story changes. Production...
Let’s imagine a scenario: It’s been nine months and a mid-sized B2B company has spent $250K. Where have they gotten to? They have transitioned from smarter lead scoring to automated forecasts. But what happened at the end? They had no model in production and honestly,...
Teams buy tools fast, then try to architect later. A warehouse here, a lake there, a feature store bolt-on, and a new GenAI gateway because the CEO asked for it. At first, it feels like progress. Then the cracks show. Ad-hoc platform sprawl creates duplicate data....
Previously, generative AI was limited to pilot decks, but gradually it has now seeped into daily processes in pharma and life sciences. AI models are now used by medical affairs, clinical and commercial teams for various processes such as scanning literature,...
Classic ML shipped with clean report cards. Accuracy, precision, recall, F1. You could argue about tradeoffs, but at least everyone agreed on what “good” meant. LLM apps don’t give you that comfort. RAG answers can be fluent and wrong. Copilots can...