Rocket Money x Hugging Face: Scaling Volatile ML Models in Production
Rocket Money partnered with Hugging Face to deploy and scale ML models in production, addressing challenges around volatile workloads. The case study covers infrastructure patterns for handling unpredictable demand in a fintech ML context. It represents a practical deployment example of Hugging Face's enterprise inference and hosting offerings.
Related guides (3)
Related events (8)
Introducing HUGS - Scale your AI with Open Models
Hugging Face announced HUGS (Hugging Face Generative Services), a new product aimed at helping enterprises scale AI deployments using open models. The service appears to target production inference infrastructure for open-weight models, positioning Hugging Face as a managed deployment layer. This is a product launch in the enterprise AI infrastructure space, competing with managed inference offerings from other providers.
Deploy LLMs with Hugging Face Inference Endpoints
Hugging Face published a guide on deploying large language models using their Inference Endpoints service. The post covers how to set up scalable, production-ready LLM deployments with minimal infrastructure overhead. It targets developers looking to move from experimentation to hosted inference without managing raw compute.
Accelerating Hugging Face Transformers with AWS Inferentia2
Hugging Face published a blog post detailing how to accelerate Transformer model inference using AWS Inferentia2, Amazon's second-generation ML inference chip. The post covers integration patterns between the Hugging Face ecosystem and the Neuron SDK for deploying models on Inferentia2 hardware. This represents a practical guide for enterprise and cloud-based inference deployment using dedicated AI accelerators.
Deploy Hugging Face Models Easily with Amazon SageMaker
Hugging Face and Amazon SageMaker announced an integration enabling streamlined deployment of Hugging Face models via SageMaker's managed infrastructure. The partnership provides dedicated Hugging Face Deep Learning Containers on AWS, simplifying the path from model hub to production inference. This represents an early milestone in the enterprise deployment pattern of hosted model hubs integrating with cloud ML platforms.
Hugging Face Teams Up with Protect AI: Enhancing Model Security for the ML Community
Hugging Face has announced a partnership with Protect AI to improve security for machine learning models hosted on the platform. The collaboration aims to address vulnerabilities in model files and supply chain risks that affect the broader ML community. Specific details about the technical implementation and scope of the security enhancements are not provided in the available content.
Databricks + Hugging Face Integration Achieves Up to 40% Faster LLM Training and Tuning
Databricks and Hugging Face have published a case study describing their integration that delivers up to 40% faster training and fine-tuning of large language models. The collaboration leverages Databricks' distributed compute infrastructure alongside Hugging Face's model hub and training libraries. This represents a practical infrastructure optimization for enterprise teams running LLM workloads on Databricks.
Deploy models on AWS Inferentia2 from Hugging Face
Hugging Face has announced support for deploying models on AWS Inferentia2 via Hugging Face Inference Endpoints. The integration allows users to deploy popular open-weight models on AWS's custom ML accelerator chips directly from the Hugging Face Hub. This expands the hardware options available for cost-effective inference beyond standard GPU instances.
The Partnership: Amazon SageMaker and Hugging Face
Hugging Face and Amazon announced a partnership integrating Hugging Face models and tools natively into Amazon SageMaker. This collaboration enables developers to train and deploy Hugging Face Transformers models directly within SageMaker's managed ML infrastructure. The partnership represents an early major cloud-provider integration for Hugging Face, expanding enterprise access to open-source NLP models.