MLOps is the set of practices used to move machine learning from experiments into reliable production services. It connects data preparation, training pipelines, experiment tracking, model registries, automated testing, deployment, monitoring, and retraining with software engineering and operational controls. Unlike ordinary application code, a model can degrade when incoming data or real-world behavior changes even if no code was modified. MLOps therefore tracks data quality, performance, drift, lineage, versions, and infrastructure alongside uptime and errors. Effective implementation also defines approval gates, rollback procedures, security, cost controls, and clear ownership among data scientists, engineers, platform teams, and domain experts.
Xiaomi has launched two open-source AI models optimized for agent-based tasks with high efficiency and low cost. The release targets growing demand for scalable enterprise AI.
Meta has signed a deal with Overview Energy to beam solar power from space to Earth. The project aims to supply constant energy for AI data centers, even at night.
Google Pentagon AI deal enables classified use of models for government operations, highlighting growing military adoption and debate over AI safeguards
OpenAI is reportedly exploring a smartphone built around AI agents instead of traditional apps. The device could reshape how users interact with software and services.
Attackers are using fake install guides for popular developer tools to trick users into running malicious commands. The campaign exploits trusted workflows like copy-paste terminal installs.
Cursor-powered AI agent deleted PocketOS’s production database and backups in seconds after acting autonomously.
OpenAI and Microsoft have revised their partnership to cap revenue sharing and allow broader cloud distribution. The changes reflect growing competition and OpenAI’s push for flexibility.
China’s top economic planner has ordered Meta to unwind its $2 billion acquisition of AI startup Manus. The decision underscores tightening controls on foreign access to Chinese AI technology.
Anthropic ran an internal experiment where AI agents negotiated and closed real-world transactions between employees. The results show stronger models secure better deals, often without users noticing.
Google plans to invest up to $40 billion in Anthropic while expanding cloud and chip support. The deal underscores the growing importance of compute capacity in the AI race.