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.
HuggingFace is ending the paid tier of the Inference API and introducing a new pricing structure including Inference Endpoints.
Considering vendor lock-in and cost issues, Kurly chose Kubeflow over AWS SageMaker and optimized its production environment by applying Elyra, SealedSecrets, and more.
Kurly built its own MLOps platform using Karpenter and NVDP to implement automatic allocation and deprovisioning of GPU resources.
This introduces how to use Inference Endpoints, which allow easy and secure deployment of models from the Hugging Face Hub to managed infrastructure.
It covers how to deploy a Vision Transformer model into a scalable environment using TensorFlow Serving and Kubernetes.
Hugging Face has launched Enterprise Hub, integrating enterprise-grade security and collaboration tools.
We operate an internal SaaS platform that supports the entire process from data collection to ML/AI utilization for Kakao crew members.
The Kakao Ad Recommendation Team integrates six optimization models and real-time data pipelines into an MLOps structure to build an advanced serving system for large-scale RTB environments.
Kakao will reveal eight AI technology sessions at the @if(kakao)2021 conference, including inference 10 times faster than TensorFlow Serving and BERT training optimization.
Pre-registration offers emoticons, with AI technology sessions on MLOps, voice, and vision for developers.
Hugging Face has released Optimum, an open-source library that supports hardware-specific performance optimization for transformer models.