Modern ML systems do not succeed because of models alone - they succeed because of the software engineering wrapped around them. Most real-world failures in MLOps come from poor structure, missing configuration, messy environments, unclear APIs, or nonexistent logging, not from bad ML.
The model's other capabilities, including support for multimodal inputs, multiple reasoning modes, and parallel sub-agents for complex queries, could help enterprises build faster, task-focused AI for customer support, automation, and internal copilots without relying on heavier models.
What happens under the hood? How is the search engine able to take that simple query, look for images in the billions, trillions of images that are available online? How is it able to find this one or similar photos from all that? Usually, there is an embedding model that is doing this work behind the hood.
