AI Briefing#mlops
The latest AI and developer news about #mlops, with the original source and a short summary.
The latest AI and developer news about #mlops, with the original source and a short summary.
The system evolved from heuristics to MAB and then to GMM blending to optimize ad revenue.
This article introduces a process that reduces MLOps tool development time by 70% and minimizes failure costs through AI pair programming.
AI reduced deployment note generation time by 80% and automated risk analysis based on past incident cases.
Hugging Face has released a new CLI called `hf` with a redesigned command structure, along with `hf jobs`, a serverless job execution service.
Hugging Face announced a real-time analytics dashboard update for monitoring and debugging Inference Endpoints.
Kakao Pay introduced an adaptive ML model to its FDS that continuously learns and adapts itself to respond to changing fraud patterns.
Hugging Face is introducing a new CAS-based upload/download architecture for efficient transfer of large models and datasets.
Over 90 sessions on AI, mobility, and infrastructure will be held over three days from October 22 to 24 at the Kakao AI Campus.
Kakao shares its AI technology application cases across six tracks, including AI agents, platforms, and multimodal AI.
Kakao revealed a real-time ad serving bucket system and LLM-based spam classification with reasoning capabilities.
Google Cloud TPU v5e is now available in Hugging Face's Inference Endpoints and Spaces.
Kurly's Data Service Development Team built a zero-downtime real-time serving system for its cart recommendation model through TorchServe tuning and GracefulShutdown implementation.
The event will be held on December 12 at Kakao Agit in Pangyo, with online live streaming and recorded videos also provided.
With Renumics Spotlight, you can interactively visualize and analyze Hugging Face datasets with a single line of code.
It guides how to deploy high-performance embedding models via Hugging Face Inference Endpoints.
Rocket Money successfully deployed a BERT model with more than 4,000 classes into production using the Hugging Face Inference API.
Fetch optimized its ML pipeline using SageMaker and Hugging Face, reducing latency by 50% and increasing accuracy by 200%.
Hugging Face Hub has joined AWS Marketplace, enabling unified billing through AWS accounts.
It introduces how to deploy LLMs using Hugging Face's managed SaaS solution, Inference Endpoints, and its key features.
Hugging Face revealed the performance and cost benefits gained from migrating its internal model serving from AWS ECS to Inference Endpoints.
AWS held a hands-on MLOps and large-scale distributed training workshop for domestic customers, sharing SageMaker-based data mesh construction and Trainium use cases.
Kurly's Data Science Team shared operational principles for demand forecasting models and leakage prevention techniques.
Kakao Pay built an AWS-based MLOps training pipeline to improve model management efficiency.
This introduces the step-by-step model inference and deployment solutions offered by Hugging Face.