Training
We track 16 posts about Training from 12 engineering blogs. Most active: Lerner Consulting Blog, Red Hat, Facebook. Latest post: Oct 13, 2026.
Companies writing about Training
Recent posts
CASTLE: training natural language search before the first real query (opens on the source site)
Airbnb ·
How we addressed the cold-start problem for Airbnb’s natural language search by jointly generating synthetic training data — queries and relevance labels — from de-identified booking and search signals.By: Wendy Ran Wei, Hao Li, Weiwei Guo, Xiaowei Liu, Xueyin Chen, Chun How Tan, Malay Haldar, Soumyadip Banerjee, Kedar Bellare, Huiji Gao, Sanjeev Katariya, and Stephanie MoyermanIntroductionNatural language search lets guests express intent in their own words, such as “romantic cabin near skiing” or “pet-friendly beach house that sleeps eight,” rather than navigating filters and categories. We…
From post-training to production: How TinyFish runs its web-native model on Databricks AI Runtime (opens on the source site)
TinyFish builds infrastructure for enterprises that need agents to work across the live web. Its Search, Fetch, Browser...
From Training to Production, NVIDIA and CoreWeave Close the Loop on Agentic AI (opens on the source site)
Nvidia ·
Building on nearly a decade of co-engineering, CoreWeave has built NVIDIA compute, networking and software into a cloud purpose-built for AI that’s still returning on investment across multiple generations of deployment. Now, CoreWeave is bringing the next generation of NVIDIA infrastructure to production. At CoreWeave Fully Connected, running this week in San Francisco, CoreWeave announced […]
Distributed training on OpenShift AI 3.4 with Kubeflow Trainer v2 (opens on the source site)
Red Hat ·
Until recently, running distributed training on Kubernetes meant picking a framework-specific custom resource definition (CRD): PyTorchJob for PyTorch, TFJob for TensorFlow, MPIJob for Message Passing Interface (MPI). Each had different semantics, different failure behavior, and different ways of configuring the same fundamentals: how many nodes, how they find each other, and what happens when something breaks. The post Distributed training on OpenShift AI 3.4 with Kubeflow Trainer v2 appeared first on Red Hat Developer.
Get Python help, one on one: my coaching sessions (opens on the source site)
Some of the most satisfying work I do happens one on one. You arrive with a real problem from your real job — code that will not behave, an architecture […] The post Get Python help, one on one: my coaching sessions appeared first on LernerPython.
Get Python help, one on one: my coaching sessions (opens on the source site)
Some of the most satisfying work I do happens one on one. You arrive with a real problem from your real job — code that will not behave, an architecture […] The post Get Python help, one on one: my coaching sessions appeared first on LernerPython.
Have it both ways: stay discoverable in search while disallowing AI training (opens on the source site)
Cloudflare is giving site owners a way to stay discoverable while disallowing AI training. New controls and an Accountable designation establish a shared model with Apple, Google, and Microsoft.
JSON Schema Design for Document AI: How to Structure ML Training Data That Actually Works (opens on the source site)
null Continue reading JSON Schema Design for Document AI: How to Structure ML Training Data That Actually Works on SitePoint.
Claude Code always produces something. That’s the hard part. (opens on the source site)
I've been using Claude Code several hours a day for months, and I'm having a blast. But an agent does what you tell it, not what you meant — which is why validating your results now matters more than the results themselves. The post Claude Code always produces something. That’s the hard part. appeared first on LernerPython.
GRPO fine-tuning on Red Hat OpenShift AI: Reinforcement learning from verifiable rewards with Training Hub (opens on the source site)
Red Hat ·
Large language models excel at general conversation, but many enterprise use cases demand structured reasoning: generating valid tool calls, writing correct SQL, producing well-formed JSON, or solving multi-step problems. Supervised fine-tuning (SFT) can improve these capabilities by showing the model labelled examples of correct outputs, but curating that training data is expensive and slow. The post GRPO fine-tuning on Red Hat OpenShift AI: Reinforcement learning from verifiable rewards with Training Hub appeared first on Red Hat Developer.
MTIA 300: Meta’s First Training Chip with Built-in NICs and Communication-Offloading Engines (opens on the source site)
Facebook ·
MTIA 300 is the first of Meta’s family of in-house training and inference accelerators optimized for training ranking and recommendation models. We’re sharing how MTIA 300’s built-in NIC chiplets allow it to meet the communication needs associated with training recommendation models with superior performance over general-purpose GPUs. By co-designing MTIA’s communication library, HCCL, alongside the [...] Read More... The post MTIA 300: Meta’s First Training Chip with Built-in NICs and Communication-Offloading Engines appeared first on Engineering at Meta.
How Guideless uses Tinybird to build training people actually finish (opens on the source site)
Tinybird ·
Learn how Guideless used Tinybird to launch real-time product analytics without building a separate analytics backend, save weeks of engineering work, and turn engagement data into insights that help users create more effective training.
GEM Training: How Meta Doubled the Efficiency of Its LLM-Scale Ads Foundation Model (opens on the source site)
Facebook ·
Meta’s Generative Ads Recommendation Model (GEM), the foundation model behind ads recommendations across Instagram and Facebook, now trains at LLM scale on several thousand of the latest-generation GPUs. This post goes into the details on how we achieved: doubling end-to-end (E2E) training efficiency to 20–25% Model FLOPs Utilization (MFU) while scaling training FLOPs 4x in [...] Read More... The post GEM Training: How Meta Doubled the Efficiency of Its LLM-Scale Ads Foundation Model appeared first on Engineering at Meta.
GPT-Red: OpenAI Is Training Models to Break Other Models (opens on the source site)
Prompt injection is still one of the least comfortable problems in AI development. You can improve your system prompt, restrict tools, validate outputs, and add approval steps before sensitive actions. Still, the model eventually has to read data you do not control. It might browse a webpage, process an email, inspect a repository, or use […] The post GPT-Red: OpenAI Is Training Models to Break Other Models appeared first on RisingStack Engineering.
Achieving Near-Linear Training Scalability for Pinterest’s Foundation Models (opens on the source site)
Sheng Huang | Software Engineer, AI Platform; Pong Eksombatchai | Machine Learning Engineer, Applied Sciences; Saurabh Vishwas Joshi | Software Engineer, AI Platform; Gaurav Arora | Software Engineer, AI Platform; Karthik Anantha Padmanabhan | Engineering Director, AI PlatformAt Pinterest, foundation models power recommendations for over 600 million monthly active users. Our latest Foundation Model (ACM RecSys 2025) pre-trains on two years of user activity data and is deployed into Home feed and Related Pins ranking, the platform’s two most important recommendation systems. Multi-node…
Next-Level Development: Harnessing AI with AIDE (opens on the source site)
If it’s worth doing by hand, it’s worth automating. Just because not everyone is (yet) a world-class developer; that doesn’t mean we can’t step closer to that expert-level space. In this post, I will introduce AIDE (Artifical Intelligence Development Environment), a powerful workflow that merges AI-driven code generation with a sharp focus on documentation-driven development. With AIDE, I tap into the best of artificial intelligence (AI) while respecting the real human insight needed for domain-specific logic. The result? An environment that streamlines repetitive coding, synchronises…
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This page is generated automatically from the engineering blogs we follow. Every post links to its source, where it was published. See all sources.