AI Briefing#model-deployment
The latest AI and developer news about #model-deployment, with the original source and a short summary.
The latest AI and developer news about #model-deployment, with the original source and a short summary.
The research demonstrated a model running on 15 Android TV boxes at 0.39 tokens per second and agent-driven deployment success rates of up to 84%.
Instead of using hosted APIs, Netflix built its own LLM serving infrastructure to directly operate the entire stack from model deployment to inference.
A deep-link integration has been launched that lets you instantly fine-tune or deploy Hugging Face models in Amazon SageMaker Studio.
One-click model deployment via FriendliAI is now available on the Hugging Face Hub.
Hugging Face now supports AWS Inferentia2, enabling deployment of over 100,000 models on SageMaker and Inference Endpoints.
AI21 Labs breaks down LLM-based product development into four stages: preparation, building, deployment, and monitoring.
Hugging Face and Microsoft have partnered to integrate the Hugging Face model catalog into Azure Machine Learning.
This introduces the step-by-step model inference and deployment solutions offered by Hugging Face.
This introduces how to use Inference Endpoints, which allow easy and secure deployment of models from the Hugging Face Hub to managed infrastructure.
Hugging Face has released the Skops library, which supports hub hosting and documentation of scikit-learn models.
It presented principles for LLM providers to prevent misuse and document safety.
spaCy NLP pipelines can now be shared, installed, and used via the Inference API on the Hugging Face Hub.
A new inference solution was unveiled through the collaboration between Hugging Face and Amazon SageMaker, making it easy to deploy transformer models.