An epoch represents one full pass through a model’s training dataset. Because examples are usually processed in smaller batches, an epoch contains multiple parameter-update steps; the exact number depends on dataset and batch size. Models often train for many epochs so optimization can progressively reduce loss. Too few passes may leave the model undertrained, while too many can waste compute or increase overfitting. Training order is commonly shuffled between epochs, and distributed or streaming setups can make the definition less literal. Teams monitor training and validation curves, save checkpoints, and use schedules or early stopping rather than assuming a fixed epoch count is optimal for every dataset.
Andrew Tulloch, co-founder of the $12 billion AI startup Thinking Machines Lab, has joined Meta after previously rejecting what reports described as a $1.5 billion offer — a figure Meta has since called ‘inaccurate and ridiculous.’
OpenAI has announced a strategic partnership with Broadcom to design and deploy 10 gigawatts of custom AI accelerators, expanding its hardware ecosystem beyond Nvidia and AMD.
Google has unveiled Gemini Enterprise, a next-generation AI platform designed for business productivity and collaboration, positioning itself directly against Microsoft’s Copilot and OpenAI’s enterprise solutions in the race for workplace AI dominance.
Google Cloud and Figma have launched an expanded partnership to embed Google’s generative AI models – including Gemini 2.5 Flash and Imagen 4 – directly into Figma’s design tools, reducing latency and accelerating creative workflows.