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 details how GoTerra migrated from BigQuery to MaxCompute and used Resource Advisor, tiered quota strategies, and the TopN Fair scheduler to dynamically balance performance and cost across dozens of accounts and hundreds of quota groups, achieving up to 60% cost reduction.
This guide demonstrates an end‑to‑end workflow on Alibaba Cloud PAI that uses the Cosmos world model to replace Isaac simulation for robot action data augmentation, including minimal human demonstrations, prompt‑driven data expansion, rejection sampling, IDM inverse‑kinematics extraction, imitation‑learning fine‑tuning, and model evaluation.
This article explains how unstructured multimedia data is transformed into vectors for similarity search, introduces GPU parallelism and the NN‑Descent algorithm to replace traditional HNSW indexing in OpenSearch, and presents benchmark results showing up to a thirteen‑fold speed improvement while maintaining comparable recall.
EMNLP 2025 in Suzhou will feature Alibaba Cloud’s AI platform PAI presenting four accepted papers on knowledge distillation, small‑model reasoning, distilled reasoning models, and an automated RAG benchmark framework, alongside exhibition demos, networking events, and recruitment opportunities for AI talent.
This article walks through an end‑to‑end Physical AI workflow on Alibaba Cloud PAI, covering robot teleoperation data collection, Isaac Lab‑based data augmentation and enhancement, imitation‑learning model training, distributed DLC execution, and systematic evaluation across varied visual conditions.
The EMR AI Assistant is an intelligent tool from Alibaba Cloud that streamlines EMR cluster management by offering quick resource queries, operation invocation, anomaly diagnosis, and conversational support, with detailed usage steps, policy code, and practical examples for big‑data operations.
This article outlines Alibaba Cloud's AI‑driven "Smart Sentinel" system, which tackles the three major challenges of large‑scale cloud operations—hard‑to‑detect anomalies, alarm storms, and difficult root‑cause analysis—by deploying multi‑layered detection, intelligent alarm grading, and an end‑to‑end automated response loop.
Huolala, a leading smart logistics platform serving over 14 markets and millions of users, detailed its massive Elasticsearch deployment—over 1.5 万 CPU cores, 40 billion records, 4 PB data—highlighting multi‑AZ design, serverless migration, and a comprehensive management platform that boosted performance, reduced costs, and enabled AI‑driven services.
Leapmotor’s rapid growth to one million production cars drove a shift from daily batch data to minute‑level real‑time analytics, prompting the adoption of Flink as the core engine of a multi‑layered big‑data platform that handles massive IoT signals, supports fault diagnosis, and integrates batch and streaming workloads on the cloud.
Li Auto’s data team tackled the explosion of vehicle‑telemetry data—over a trillion rows and millions of signals per second—by redesigning their data foundation with Alibaba Cloud’s Hologres and Flink, achieving sub‑second latency, elastic scaling, high availability, and significant cost reductions across real‑time and offline workloads.
