Industrial and defense environments generate massive amounts of data that can’t wait for the cloud. Latency is often measured in milliseconds, and resiliency is paramount. A manufacturing plant can’t go down due to flaky Wi-Fi or a public cloud outage. “Traditional” approaches — shipping servers, hiring local IT, bespoke development, managing one-off deployments — simply don’t scale. Critical operations …
Valentin Geffrier, Tanguy Cornuau Each year, we bring the Analytics Engineering community together for an Analytics Summit — a multi-day internal conference to share analytical deliverables across Netflix, discuss analytic practice, and build relationships within the community. This post is one of several topics presented at the Summit highlighting the breadth and impact of Analytics work across different …
Read more “Scaling Global Storytelling: Modernizing Localization Analytics at Netflix”
Deploying large language models (LLMs) for inference requires reliable GPU capacity, especially during critical evaluation periods, limited-duration production testing, or burst workloads. Capacity constraints can delay deployments and impact application performance. Customers can use Amazon SageMaker AI training plans to reserve compute capacity for specified time periods. Originally designed for training workloads, training plans now …
Read more “Deploy SageMaker AI inference endpoints with set GPU capacity using training plans”
At Google Cloud, serving the massive-scale needs of large foundation model builders and AI-native companies is at the forefront of our AI infrastructure strategy. As generative AI transitions to mission-critical production environments, these innovators require dynamic, relentlessly efficient infrastructure to overcome complex orchestration challenges and power an agentic future. To meet this moment, we are …
Read more “Kubernetes as AI Infrastructure: Google Cloud, llm-d, and the CNCF”
edit/fyi: i originally posted this on their official sub, but they literally locked the thread and deleted my post right after leaving a braindead copy-paste support reply. they are actively silencing people who expose their shady billing practices instead of just apologizing. so i’m posting it here. first off, locking into a 1 year sub …
Read more “stay away from higgsfield ai. total predatory bs with their refunds.”
Traditional search engines have historically relied on keyword search.
By Harshad Sane Ranker is one of the largest and most complex services at Netflix. Among many things, it powers the personalized rows you see on the Netflix homepage, and runs at an enormous scale. When we looked at CPU profiles for this service, one feature kept standing out: video serendipity scoring — the logic that answers a …
Read more “Optimizing Recommendation Systems with JDK’s Vector API”
Large language models (LLMs) perform well on general tasks but struggle with specialized work that requires understanding proprietary data, internal processes, and industry-specific terminology. Supervised fine-tuning (SFT) adapts LLMs to these organizational contexts. SFT can be implemented through two distinct methodologies: Parameter-Efficient Fine-Tuning (PEFT), which updates only a subset of model parameters, offering faster training …
The flexibility of Google Cloud allows enterprises to build secure and reliable architecture for their AI workloads. In this blog we will look at a reference architecture for private connectivity for retrieval-augmented generation (RAG)-capable generative AI applications. This architecture is for scenarios where communications of the overall system must use private IP addresses and must …
Read more “Designing private network connectivity for RAG-capable gen AI apps”
Gebbia was reportedly spotted at a San Francisco coffee shop using an unidentified pair of earbuds with a circular disc that looks similar to the device seen in a recent OpenAI hoax ad.