The Alibaba Cloud Big Data AI Platform builds on Alibaba’s leading cloud infrastructure, big‑data and AI engineering capabilities, scenario algorithms, and extensive industry experience to offer enterprises and developers a one‑stop, cloud‑native big‑data and AI capability suite. It boosts AI development efficiency, enables large‑scale AI deployment across industries, and drives business value.
This article examines how the Manus sandbox and CodeAct mechanisms inspire a GitOps‑based approach to building LLM agents, detailing the design of planner and executor components, workflow steps, advantages such as RAG and observability, and the potential for low‑cost, scalable intelligent agent development.
This article summarizes a Cloud Summit talk where Alibaba Cloud’s AI expert Zhang Yingying explains how large language models can enhance big‑data intelligent operations, covering opportunities, challenges, RAG‑based Q&A, multi‑agent diagnostics, and the engineering architecture needed for reliable, scalable deployment.
This article traces the evolution of Alibaba’s intelligent agents from the initial chain‑of‑thought design through instantiation, structuring, self‑evolution, and middleware integration, highlighting practical challenges, architectural refinements, and open‑source tools for large‑scale AI operations.
The article explores how Von Neumann’s insights on the brain‑computer analogy illuminate modern large‑language‑model prompt engineering, comparing logical reasoning chains, memory mechanisms, and DSL‑driven computation to improve accuracy, reduce hallucinations, and balance reasoning depth with precise calculation.
This article introduces the GSPO (Group Sequence Policy Optimization) reinforcement learning algorithm, explains its advantages over GRPO, and provides a detailed, end‑to‑end tutorial for reproducing GSPO training on Alibaba Cloud's PAI platform using the PAI‑ChatLearn framework.
This article explains how Alibaba Cloud’s big‑data platform leverages Flink ML to build an intelligent‑operations service that tackles stability, cost and efficiency challenges through time‑series anomaly detection, forecasting and streaming log‑clustering, dramatically reducing latency, complexity and operational overhead.
This article details Alibaba's decade‑long evolution of its real‑time computing platform, the massive operational challenges of managing Flink clusters at million‑core scale, and the comprehensive strategies—including SLA metrics, self‑healing services, cloud‑native redesign, and job‑level advisory tools—used to ensure stability, cost efficiency, and performance during peak events like Double‑11.
This article details Alibaba Cloud's intelligent operations team’s challenges and solutions for managing thousands of cloud‑native clusters, covering their multi‑layered operation architecture, GitOps workflow, infrastructure‑as‑code integration, and the role of AI‑driven intelligent operations in large‑scale environments.
This article details how the open‑source SREWorks frontend evolved through a monorepo restructure, selective use of Webpack and Rollup, remote component loading, and build optimizations to achieve a more extensible, plug‑in‑friendly low‑code platform for cloud‑native operations.
This article introduces the Open Application Model (OAM), explains its key features—application‑first design, clear extensibility, and cloud‑agnosticism—and demonstrates how Alibaba’s SREWorks leverages OAM to enable modular, plug‑in‑driven, multi‑cloud, scalable application deployment, management, and lifecycle operations.
