Harnessing CPUs for Practical, Cost-Effective Machine Learning The post How to Benchmark Classical Machine Learning Workloads on Google Cloud appeared first on Towards Data Science. Generated by RSStT. The copyright belongs to the original author. Source

At Grab, our decade-old Go monorepo had become a 214GB monster with 13 million commits, causing 4-minute replication delays and crippling developer productivity. Through custom migration tooling and strategic history pruning, we achieved a 99.9% reduction…

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Dive into Grab’s engineering journey to optimise a core ML model. Learn how we built the Triton Server Manager and used Triton Inference Server (TIS) to achieve a 50% reduction in tail latency and seamlessly migrate over 50% of online deployments.

Explore how Grab uses machine learning to perform predictive scaling on our data processing workloads.

e-KYC faces challenges with unstandardized document formats and local SEA languages. Existing LLMs lack sufficient SEA language support. We trained a Vision LLM from scratch, modifying open-source models to be 50% faster while maintaining accuracy. These…

Discover how our transition from cloud-based Mac hardware infrastructure to a colocation cluster within Southeast Asia has revolutionized our macOS CI/CD, enhancing performance and reducing costs.

Discover SpellVault’s evolution from its early RAG-based foundations and plugin ecosystem to its transformation into a tool-driven, agentic framework that empowers users to build AI agents that are powerful, flexible, and future-ready.

Discover how Grab's Coban Platform revolutionizes real-time data quality monitoring for Kafka streams. Learn how syntactic and semantic tests empower stream users to ensure reliable data, prevent cascading errors, and accelerate AI-driven innovation.

Discover how Grab has championed cybersecurity for a decade with its Bug Bounty Program. This article delves into the milestones, insights, and the collaborative efforts that have fortified Grab's defenses, ensuring a secure and reliable platform for millions.

Indian and global IT workers are sounding alarms about the widespread misuse of Performance Improvement Plans (PIPs) to pressure employees to resign, often without a clear link to actual performance. Workers’ accounts and HR leaders’ admissions reveal PIPs…