GCP
GCP on Beyond Market Intelligence: a running collection of 3 stories we have gathered and hand-picked because they are worth your time. Every post here touches on gcp in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around gcp, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
Article: Eliminating Long-Lived Credentials in GCP with Workload Identity Federation
Long-lived service account keys in Google Cloud Platform (GCP) represent a persistent security challenge—difficult to rotate and prone to leakage. Our analysis of scaling Workload Identity Federation across 120+ production projects demonstrates a fundamental shift in machine identity management. Rather than managing secrets, this approach establishes trust relationships, configured once and secured by attribute conditions. Explore how this paradigm change eliminates credential sprawl and enhances overall security.
28 TPS on Qwen2.5-7B across two separate cloud regions over public WAN using speculative decoding + CUDA Graphs [P]
Recent benchmarking demonstrates transformative gains in distributed LLM inference using ShardFlow, a new framework designed to split HuggingFace transformers across multiple GPUs. Utilizing speculative decoding and CUDA Graphs, we achieved a peak of 28.10 tokens per second (TPS) on Qwen2.5-7B across two geographically separated GCP regions (Iowa & Oregon) connected via a public WAN – a setup exhibiting approximately 86ms round-trip latency. This highlights a key insight: speculative decoding minimizes latency impact, shifting costs from per-token to per-round.
Avoiding Entity Key Drift in a Data Lake: Step 1, Normalization
High-frequency streaming data pipelines against live APIs expose critical challenges, particularly entity key drift in data lakes. This four-part series tackles that challenge, beginning with normalization, a foundational step for maintaining data integrity. We'll explore building a robust pipeline using openSenseMap—a citizen-science IoT network—demonstrating how real-world data quality issues demand innovative solutions. Subsequent articles will cover matching, polling, noise filtering, and a vendor-agnostic Iceberg pipeline. Interested in broader data science considerations? See "What Professionals Should Know About Data Science and AI" for vital insights.