InfoQ Homepage Machine Learning Content on InfoQ
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Optimize AI Workloads: Google Cloud’s Tips and Tricks
Google Cloud has announced a suite of new tools and features designed to help organizations reduce costs and improve efficiency of AI workloads across their cloud infrastructure. The announcement comes as enterprises increasingly seek ways to optimize spending on AI initiatives while maintaining performance and scalability.
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Announcing QCon AI: Focusing on Practical, Scalable AI Implementation for Engineering Teams
QCon AI focuses on practical, real-world AI for senior developers, architects, and engineering leaders. Join us Dec 16-17, 2025, in NYC to learn how teams are building and scaling AI in production—covering MLOps, system reliability, cost optimization, and more. No hype, just actionable insights from those doing the work.
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How SREs and GenAI Work Together to Decrease eBay's Downtime: an Architect's Insights at KubeCon EU
During his KubeCon EU keynote, Vijay Samuel, Principal MTS Architect at eBay, shared his team’s experience of enhancing incident response capabilities by incorporating ML and LLM building blocks. They realised that GenAIs are not a silver bullet but can help engineers through complex incident investigations through logs, traces, and dashboard explanations.
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Recap of Cloudflare Security Week 2025: From Quantum Cryptography to AI Labyrinth
During the recent Cloudflare Security Week 2025, the cloud provider announced various improvements to its cybersecurity services and multiple reports analyzing trends and challenges in security threats. Additionally, they announced AI Labyrinth, a new version of honeypots against unauthorized crawlers, and Cloudflare for AI, a suite of tools aimed at helping the adoption of secure AI technologies.
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Azure AI Foundry Supports NVIDIA NIM and AgentIQ for AI Agents
Microsoft and NVIDIA have teamed up to integrate NVIDIA NIM microservices and AgentIQ into Azure AI Foundry, streamlining AI agent application development. This partnership accelerates project lifecycles, optimizing performance and reducing costs. The toolkit enhances AI efficiency through real-time telemetry, enabling effortless deployment and advanced functionalities for developers.
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Google DeepMind Unveils Gemini Robotics
Google DeepMind has introduced Gemini Robotics, an advanced AI model designed to enhance robotics by integrating vision, language, and action. This innovation, based on the Gemini 2.0 framework, aims to make robots smarter and more capable, particularly in real-world settings.
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Meta Unifies Facebook’s Video Delivery System across Mobile and Web Apps
Meta finalized efforts to consolidate Facebook’s video delivery system by migrating video experiences from older Watch product to more recent Reels product, which became the basis of the unified system. The unification process required changes across mobile UI, server backend, and ranking systems while ensuring a seamless transition for billions of users.
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instructlab.ai Uses Synthetic Data to Reduce Complexity of Fine-Tuning LLMs
InstructLab.ai implements the large-scale alignment for the chatbots concept(LAB), which intends to overcome the scalability challenges in the instruction-tuning phase of a large language model (LLM). Its approach leverages a synthetic data-based alignment tuning method for LLMs. Crafted taxonomies deliver the synthesization seeds for training data, reducing the need for human-annotated data.
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OpenAI Features New o3-mini Model on Microsoft Azure OpenAI Service
OpenAI has launched the advanced o3-mini model via Microsoft Azure, enhancing AI applications with improved cost efficiency, faster performance, and adjustable reasoning capabilities. Designed for complex tasks, it supports structured outputs and backward compatibility. With widespread access, the o3-mini empowers developers to drive innovation across various industries.
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OpenEuroLLM: Europe’s New Initiative for Open-Source AI Development
A consortium of 20 European research institutions, companies, and EuroHPC centers has launched OpenEuroLLM, an initiative to develop open-source, multilingual large language models (LLMs). Coordinated by Jan Hajič and co-led by Peter Sarlin, the project aims to provide transparent and compliant AI models for commercial and public sector applications.
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OpenAI Launches Deep Research: Advancing AI-Assisted Investigation
OpenAI has launched Deep Research, a new agent within ChatGPT designed to conduct in-depth, multi-step investigations across the web. Initially available to Pro users, with plans to expand access to Plus and Team users, Deep Research automates time-consuming research by retrieving, analyzing, and synthesizing online information.
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Block Launches Open-Source AI Framework Codename Goose
Block’s Open Source Program Office has launched Codename Goose, an open-source, non-commercial AI agent framework designed to automate tasks and integrate seamlessly with existing tools. Goose provides users with a flexible, on-machine AI assistant that can be customized through extensions, enabling developers and other professionals to enhance their productivity.
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AMD and Johns Hopkins Researchers Develop AI Agent Framework to Automate Scientific Research Process
Researchers from AMD and Johns Hopkins University have developed Agent Laboratory, an artificial intelligence framework that automates core aspects of the scientific research process. The system uses large language models to handle literature reviews, experimentation, and report writing, producing both code repositories and research documentation.
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Synthetic Data Generator Simplifies Dataset Creation with Large Language Models
Hugging Face has introduced the Synthetic Data Generator, a new tool leveraging Large Language Models (LLMs), that offers a streamlined, no-code approach to creating custom datasets. The tool facilitates the creation of text classification and chat datasets through a clear and accessible process, making it usable for both non-technical users and experienced AI practitioners.
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Using Machine Learning on Microcontrollers: Decreasing Memory and CPU Usage to Save Power and Cost
According to Eirik Midttun, artificial intelligence (AI) and machine learning (ML) are useful tools for interpreting sensor data, especially when the input is complex, such as vibration, voice, and vision. The main challenges of using machine learning on microcontrollers are the constraints in computing power available and cost-related requirements that come with microcontroller-based designs,