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Simplifying and Accelerating Modern Workloads

Architecture and Considerations in Machine Learning Pipeline

Top tips to boost your training speed by 5-10x with lower cost, including code snippets and real-world use cases

Bringing a large language model from its initial training to deployment requires numerous systems and components. At Zhihu, we grappled with a multi-cloud, cross-region AI platform, requiring an efficient solution to facilitate the rapid training and delivery of models for production use cases. This led us to adopt Alluxio, the high-performance data access layer for LLM. This blog provides an in-depth look at Zhihu’s challenges, journey, and solution for LLM training and deployment. Through adopting Alluxio, we’ve significantly enhanced model training performance by 2 to 3 times and can deploy updated models every minute instead of hours or days. Also, our GPU utilization has doubled, infrastructure and operation costs have been halved, and we have established a resilient, efficient infrastructure capable of meeting our escalating AI demands.

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Best Practices and Tuning Tips (with SQL codes, configuration settings, session properties, examples, and real-world case studies!)

Best Practices and Tuning Tips (with SQL codes, configuration settings, session properties, examples, and real-world case studies!)

Query Acceleration & Data Access as a Service

Best Practices for Cloud Cost Optimization