Developers are embracing AI tools faster than ever (84% adoption) but trust is crashing. Only 33% trust AI accuracy, down from 43%. The culprit? 'Almost right' code that takes longer to debug than writing from scratch.
Good Morning from San Francisco, OpenAI plants its flag in a Norwegian fjord. The company will spend $1 billion on
Apple's bet on India to escape China risks just hit Trump's tariff wall. Stock down 17% as AI strategy fails while competitors soar. The company that once led tech innovation now trades at premium prices for mature-company growth.
Category: Technical Terms
MLOps applies software engineering practices to machine learning, making AI systems reliable and maintainable in production.
MLOps covers the entire AI lifecycle: version control for models and data, automated testing, deployment pipelines, and monitoring system performance.
It treats AI models like software applications that need proper engineering practices.
Without MLOps, AI projects often fail when moving from research to real applications. Models break, data drifts, and performance degrades without proper monitoring.
MLOps makes AI systems robust enough for business use.
Get the 5-minute Silicon Valley AI briefing, every weekday morning — free.
