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.
OpenAI and Databricks have forged a multiyear $100 million partnership to embed powerful AI agents into enterprise data platforms, enabling organizations to build agents using their own data.
Google is integrating Gemini AI into Chrome for U.S. users, bringing features like multi-tab summarization, agentic task automation, and deeper sync with Google apps.
AI search startup Perplexity has reportedly raised $200 million at a $20 billion valuation, just two months after a $100 million round – signaling its rapid rise as a major rival to Google.
IBM has unveiled a detailed development plan for Quantum Starling, the world’s first large-scale fault-tolerant quantum computer, aiming for deployment by 2029 – setting a milestone for practical quantum capability.