InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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Grab Adds Real-Time Data Quality Monitoring to Its Platform
Grab updated its internal platform to monitor Apache Kafka data quality in real time. The system uses FlinkSQL and an LLM to detect syntactic and semantic errors. It currently tracks 100+ topics, preventing invalid data from reaching downstream users. This proactive strategy aligns with industry trends to treat data streams as reliable products.
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Karrot Improves Conversion Rates by 70% with New Scalable Feature Platform on AWS
Karrot replaced its legacy recommendation system with a scalable architecture that leverages various AWS services. The company sought to address challenges related to tight coupling, limited scalability, and poor reliability in its previous solution, opting instead for a distributed, event-driven architecture built on top of scalable cloud services.
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Growing Yourself as a Software Engineer, Using AI to Develop Software
Sharing your work as a software engineer inspires others, invites feedback, and fosters personal growth, Suhail Patel said at QCon London. Normalizing and owning incidents builds trust, and it supports understanding the complexities. AI enables automation but needs proper guidance, context, and security guardrails.
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Arm Launches AI-Powered Copilot Assistant to Migrate Workflows to Arm Cloud Compute
At the recent GitHub Universe 2025 developer conference, Arm unveiled the Cloud migration assistant custom agent, a tool designed to help developers automate, optimize, and accelerate the migration of their x86 cloud workflows to Arm infrastructure.
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Memori Expands into a Full-Scale Memory Layer for AI Agents across SQL and MongoDB
Memori is an innovative, open-source memory system that empowers AI agents with structured, long-term memory using standard databases like SQL and MongoDB. It seamlessly integrates into existing frameworks, enabling efficient data extraction and retrieval without vendor lock-in. Ideal for developers, Memori's modular design ensures reliability and scalability for next-gen intelligent systems.
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How Discord Scaled its ML Platform from Single-GPU Workflows to a Shared Ray Cluster
Discord has detailed how it rebuilt its machine learning platform after hitting the limits of single-GPU training. The changes enabled daily retrains for large models and contributed to a 200% uplift in a key ads ranking metric.
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Google Introduces Nano Banana Pro with Grounded, Multimodal Image Synthesis
Google has released Nano Banana Pro. The system moves beyond conventional diffusion workflows by tightly coupling image generation with Gemini’s multimodal reasoning stack. The result: visuals that are not only aesthetically pleasing, but structurally, contextually, and informationally accurate.
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Google's New LiteRT Accelerator Supercharges AI Workloads on Snapdragon-powered Android Devices
Google has introduced a new accelerator for LiteRT, called Qualcomm AI Engine Direct (QNN), to enhance on-device AI performance on Qualcomm-powered Android devices equipped with Snapdragon 8 SoCs. The accelerator delivers significant gains, offering up to a 100x speedup over CPU execution and 10x over GPU.
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Private AI Compute Enables Google Inference with Hardware Isolation and Ephemeral Data Design
Google announced Private AI Compute, a system designed to process AI requests using Gemini cloud models while aiming to keep user data private. The announcement positions Private AI Compute as Google's approach to addressing privacy concerns while providing cloud-based AI capabilities, building on what the company calls privacy-enhancing technologies it has developed for AI use cases.
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KubeCon NA 2025 - Robert Nishihara on Open Source AI Compute with Kubernetes, Ray, PyTorch, and vLLM
AI workloads are growing more complex in terms of compute and data, and technologies like Kubernetes and PyTorch can help build production-ready AI systems to support them. Robert Nishihara from Anyscale recently spoke at KubeCon + CloudNativeCon North America 2025 Conference about how an AI compute stack comprising Kubernetes, PyTorch, VLLM and Ray technologies can support these new AI workloads.
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Amazon Adds A2A Protocol to Bedrock AgentCore for Interoperable Multi-Agent Workflows
Amazon announced support for the Agent-to-Agent (A2A) protocol in Amazon Bedrock AgentCore Runtime, enabling communication between agents built on different frameworks. The protocol allows agents developed with Strands Agents, OpenAI Agents SDK, LangGraph, Google ADK, or Claude Agents SDK to "share context, capabilities, and reasoning in a common, verifiable format."
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LinkedIn’s Migration Journey to Serve Billions of Users by Nishant Lakshmikanth at QCon SF
Engineering Manager Nishant Lakshmikanth showcased LinkedIn's transformation at QCon SF 2025, detailing a shift from legacy batch-based systems to a real-time architecture. By decoupling recommendations and leveraging dynamic scoring techniques, LinkedIn achieved a 90% reduction in offline costs, enhanced session-level freshness, and improved member engagement while future-proofing its platform.
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SAM 3 Introduces a More Capable Segmentation Architecture for Modern Vision Workflows
Meta has released SAM 3, the latest version of its Segment Anything Model and the most substantial update to the project since its initial launch. Built to provide more stable and context-aware segmentation, the model offers improvements in accuracy, boundary quality, and robustness to real-world scenes, aiming to make segmentation more reliable across research and production systems.
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Google Launches Agent Development Kit for Go
Google has added support for the Go language to its Agent Development Kit (ADK), enabling Go developers to build and manage agents in an idiomatic way that leverages the language's strong concurrency and typing features.
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Microsoft Copilot Fall Release Includes Collaboration and Personalization Features
Microsoft's recent Copilot Fall Release includes several new features for productivity, collaboration, and personalization. The release also includes updates to Copilot features in Edge and Windows, as well as integration with Microsoft's in-house AI models.