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#model-deployment

Every item tagged model-deployment, newest first.

24 items

otherNov 24

OVHcloud on Hugging Face Inference Providers 🔥

OVHcloud has partnered with Hugging Face to offer a new inference provider on the Hugging Face Hub. This integration allows users to deploy and manage models on OVHcloud's infrastructure. Builders can now access OVHcloud's scalable and secure infrastructure for model deployment. The partnership aims to provide a seamless experience for deploying AI models.

Key takeaways
  • OVHcloud joins Hugging Face as an inference provider.
  • Deploy models on OVHcloud's infrastructure via Hugging Face Hub.
  • Scalable and secure infrastructure for model deployment.
HHugging Face Blog#inference#cloud-computing#model-deployment
otherJun 12

Featherless AI on Hugging Face Inference Providers 🔥

Featherless AI has joined Hugging Face as an inference provider, expanding access to its optimized models via Hugging Face's API. This integration allows developers to deploy Featherless models directly through Hugging Face's platform. Builders can now access Featherless' optimized models without leaving the Hugging Face ecosystem. The partnership aims to streamline model deployment and enhance developer experience.

Key takeaways
  • Featherless AI is now an inference provider on Hugging Face.
  • Developers can deploy Featherless models via Hugging Face's API.
  • Partnership aims to simplify model deployment for developers.
HHugging Face Blog#inference#hugging-face#model-deployment
otherMay 23

Dell Enterprise Hub is all you need to build AI on premises

Dell and Hugging Face have partnered to offer a comprehensive on-premises AI solution called Dell Enterprise Hub. This integrated system enables enterprises to build, deploy, and manage AI applications with Hugging Face models and tools. The partnership aims to simplify AI adoption for businesses by providing a streamlined, on-premises infrastructure. You can now deploy AI models in your own data center.

Key takeaways
  • Dell Enterprise Hub integrates Hugging Face models and tools.
  • On-premises solution for building and deploying AI applications.
  • Partnership aims to simplify AI adoption for enterprises.
HHugging Face Blog#on-premises-ai#enterprise-ai#model-deployment
otherMay 19

Microsoft and Hugging Face expand collaboration

Microsoft and Hugging Face are expanding their collaboration to make Hugging Face models more accessible on Microsoft Azure. The partnership aims to simplify model deployment and fine-tuning for builders. This integration enables easier use of Hugging Face's model hub and tools on Azure's cloud infrastructure. You can now deploy Hugging Face models directly on Azure.

Key takeaways
  • Hugging Face models are now more accessible on Microsoft Azure.
  • The partnership simplifies model deployment and fine-tuning.
  • Builders can deploy Hugging Face models directly on Azure.
HHugging Face Blog#cloud-ai#model-deployment#partnerships
otherApr 16

Cohere on Hugging Face Inference Providers 🔥

Cohere has joined Hugging Face as an inference provider, expanding access to its models through the Hugging Face ecosystem. This partnership allows developers to deploy Cohere models directly within Hugging Face's inference platform. You can now use Cohere's models with Hugging Face's tools and services. The integration aims to provide a seamless experience for deploying and managing AI models.

Key takeaways
  • Cohere models available on Hugging Face inference platform.
  • Developers can deploy Cohere models directly within Hugging Face.
  • Partnership aims to simplify AI model deployment and management.
HHugging Face Blog#inference#hugging-face#model-deployment
toolsFeb 24

Remote VAEs for decoding with Inference Endpoints 🤗

Hugging Face has introduced remote VAEs for decoding with Inference Endpoints, enabling more flexible and scalable model deployment. This feature allows users to deploy and manage models remotely, streamlining the deployment process. Builders can now focus on developing applications rather than managing infrastructure. The remote VAEs are designed to work seamlessly with Hugging Face's existing tools and services.

Key takeaways
  • Remote VAEs enable flexible and scalable model deployment.
  • Streamlines deployment process for builders.
  • Works with existing Hugging Face tools and services.
HHugging Face Blog#inference#remote-computing#model-deployment
otherFeb 18

Introducing Three New Serverless Inference Providers: Hyperbolic, Nebius AI Studio, and Novita 🔥

Hugging Face has added three new serverless inference providers: Hyperbolic, Nebius AI Studio, and Novita. This expansion offers builders more choices for deploying and serving AI models. The addition of these providers increases competition in the serverless inference market. You can now explore these new options for your AI model deployment needs.

Key takeaways
  • Three new serverless inference providers added.
  • Increased competition in serverless inference market.
  • More deployment choices for AI models.
HHugging Face Blog#serverless-inference#model-deployment#hugging-face
otherJan 28

Welcome to Inference Providers on the Hub 🔥

Hugging Face has launched Inference Providers on the Hub, a new feature that allows users to deploy and manage models from multiple providers in one place. This centralized hub enables builders to easily discover, deploy, and manage inference endpoints for various AI models. By supporting multiple providers, Hugging Face aims to simplify the deployment process and increase model accessibility. You can now explore and deploy models from different providers using the Hub.

Key takeaways
  • Centralized deployment and management of models from multiple providers.
  • Simplified deployment process for AI models.
  • Increased model accessibility through a single hub.
HHugging Face Blog#inference#model-deployment#hugging-face
otherJan 22

Hugging Face and FriendliAI partner to supercharge model deployment on the Hub

Hugging Face and FriendliAI have partnered to improve model deployment on the Hugging Face Hub. The partnership aims to provide users with more efficient and scalable model deployment options. This collaboration is expected to benefit builders who use the Hub for model hosting and deployment. The integration will enable faster and more reliable model serving.

Key takeaways
  • Hugging Face partners with FriendliAI for model deployment.
  • Partnership targets improved efficiency and scalability on the Hub.
  • Integration enables faster model serving.
HHugging Face Blog#model-deployment#hugging-face#partnerships
toolsSep 20

Optimize and deploy with Optimum-Intel and OpenVINO GenAI

Intel and Hugging Face collaborated on Optimum-Intel, a deployment optimization tool that integrates with OpenVINO GenAI for efficient model serving. This integration enables developers to optimize and deploy AI models more efficiently across Intel hardware. You can use Optimum-Intel to streamline model deployment on Intel-based infrastructure. The collaboration aims to make AI model deployment faster and more cost-effective.

Key takeaways
  • Optimum-Intel integrates with OpenVINO GenAI for optimized model deployment.
  • Targets efficient serving on Intel hardware.
  • Aims to reduce deployment costs and increase speed.
HHugging Face Blog#model-deployment#intel#openvino
modelsAug 19

Deploy Meta Llama 3.1 405B on Google Cloud Vertex AI

Meta Llama 3.1 405B is now deployable on Google Cloud Vertex AI, allowing builders to run the model in a managed environment. The integration enables access to Llama 3.1 405B's capabilities without managing infrastructure. You can deploy the model through the Vertex AI console or API.

Key takeaways
  • Llama 3.1 405B available on Google Cloud Vertex AI.
  • Managed environment simplifies deployment.
  • Access via console or API.
HHugging Face Blog#model-deployment#cloud-ai#managed-services
otherJul 9

Google Cloud TPUs made available to Hugging Face users

Google Cloud has made its Tensor Processing Units (TPUs) available to Hugging Face users through a new integration. This allows Hugging Face customers to deploy and run models on TPUs, leveraging Google's custom hardware for faster inference. Builders can now access TPUs through Hugging Face's Inference Endpoints and Spaces, enabling them to optimize model performance and reduce costs. The integration aims to provide a seamless experience for deploying AI models at scale.

Key takeaways
  • TPUs now available to Hugging Face users for model deployment.
  • Integration enables faster inference and potential cost savings.
  • Hugging Face provides access through Inference Endpoints and Spaces.
HHugging Face Blog#cloud-ai#model-deployment#hardware-acceleration
modelsMay 22

Deploy models on AWS Inferentia2 from Hugging Face

Hugging Face now supports deploying models on AWS Inferentia2, a custom chip designed for high-performance, low-cost inference. This integration allows you to deploy models with optimized performance and cost efficiency. Builders can use Inferentia2 to run models at scale while reducing infrastructure costs. The partnership aims to make AI deployment more accessible and affordable.

Key takeaways
  • Hugging Face supports AWS Inferentia2 for model deployment.
  • Inferentia2 offers high-performance, low-cost inference.
  • Partnership aims to make AI deployment more accessible.
HHugging Face Blog#model-deployment#inference-optimization#cloud-ai
otherMay 21

From cloud to developers: Hugging Face and Microsoft Deepen Collaboration

Hugging Face and Microsoft have expanded their collaboration to provide developers with easier access to Hugging Face models and datasets on Microsoft Azure. The partnership aims to simplify the deployment of AI models and enable more developers to build AI-powered applications. This collaboration targets builders who want to leverage Hugging Face's open-source AI tools and Microsoft's cloud infrastructure. The integration is designed to streamline AI model deployment and management.

Key takeaways
  • Hugging Face models and datasets now available on Microsoft Azure.
  • Simplified deployment and management of AI models.
  • Expanded access to open-source AI tools for developers.
HHugging Face Blog#open-source#cloud-ai#model-deployment
modelsFeb 1

Hugging Face Text Generation Inference available for AWS Inferentia2

Hugging Face has made Text Generation Inference available for AWS Inferentia2, enabling faster and more cost-effective deployment of text generation models on AWS. This integration allows builders to optimize model performance and reduce costs. The move targets developers looking to deploy AI models efficiently on cloud infrastructure. Inferentia2 chips provide optimized performance for machine learning workloads.

Key takeaways
  • Text Generation Inference now supported on AWS Inferentia2.
  • Enables faster and more cost-effective model deployment.
  • Optimized for Inferentia2 chips' machine learning performance.
HHugging Face Blog#cloud-ai#text-generation#model-deployment
toolsJan 14

Run ComfyUI workflows for free with Gradio on Hugging Face Spaces

You can now run ComfyUI workflows for free using Gradio on Hugging Face Spaces. This integration allows users to deploy and interact with ComfyUI models directly in the browser without requiring local setup. The setup is fully managed by Hugging Face, eliminating the need for users to handle infrastructure. Builders can focus on developing and testing their models.

Key takeaways
  • ComfyUI workflows can be run for free on Hugging Face Spaces.
  • Integration with Gradio enables browser-based deployment and interaction.
  • No local setup or infrastructure management required.
HHugging Face Blog#hugging-face#gradio#comfyui#model-deployment
modelsAug 9

Deploying Hugging Face Models with BentoML: DeepFloyd IF in Action

The DeepFloyd IF model from Hugging Face can be deployed with BentoML for scalable and efficient model serving. This integration enables builders to easily containerize and deploy AI models. By using BentoML, developers can focus on building applications rather than managing model infrastructure.

Key takeaways
  • DeepFloyd IF model deployable with BentoML.
  • Enables scalable and efficient model serving.
  • Simplifies containerization and deployment for developers.
HHugging Face Blog#model-deployment#hugging-face#bentoml
modelsMay 31

Introducing the Hugging Face LLM Inference Container for Amazon SageMaker

Hugging Face and AWS have collaborated on an LLM inference container for Amazon SageMaker, streamlining deployment of Hugging Face models on SageMaker. This integration allows for one-click deployment of Hugging Face models, enabling faster and more efficient model serving. You can deploy models with optimized performance and reduced latency. The container supports popular Hugging Face Transformers and is available for use on SageMaker.

Key takeaways
  • One-click deployment of Hugging Face models on SageMaker.
  • Optimized performance and reduced latency for model serving.
  • Supports popular Hugging Face Transformers.
HHugging Face Blog#model-deployment#cloud-ai#inference-optimization
modelsAug 19

Deploying 🤗 ViT on Vertex AI

You can deploy Hugging Face's ViT model on Google Cloud's Vertex AI for image classification tasks. The integration allows for scalable model serving and automated batching. This setup enables you to focus on building applications rather than managing infrastructure. The deployment process is streamlined through Hugging Face's tools.

Key takeaways
  • Deploy ViT on Vertex AI for scalable image classification.
  • Integration allows for automated batching and managed infrastructure.
  • Hugging Face tools simplify the deployment process.
HHugging Face Blog#model-deployment#cloud-ai#image-classification
toolsJul 25

Deploying TensorFlow Vision Models in Hugging Face with TF Serving

You can deploy TensorFlow vision models in Hugging Face using TF Serving. This integration enables seamless model deployment and management. TF Serving provides a scalable and flexible solution for serving machine learning models. Builders can leverage this integration to streamline their model deployment workflows.

Key takeaways
  • TensorFlow vision models can be deployed in Hugging Face with TF Serving.
  • TF Serving provides scalable and flexible model serving capabilities.
  • Integration enables streamlined model deployment and management workflows.
HHugging Face Blog#hugging-face#tf-serving#model-deployment#tensorflow
toolsJun 22

Convert Transformers to ONNX with Hugging Face Optimum

Hugging Face Optimum provides a seamless way to convert transformer models to ONNX format, enabling faster inference and better performance. This conversion allows for optimized deployment on various hardware platforms. You can leverage Optimum's tools to streamline your model's deployment process. The conversion process is designed to be straightforward and efficient.

Key takeaways
  • Hugging Face Optimum supports converting transformers to ONNX.
  • ONNX format enables faster inference and better performance.
  • Conversion process is straightforward and efficient.
HHugging Face Blog#onnx#transformers#model-deployment
modelsJan 11

Deploy GPT-J 6B for inference using Hugging Face Transformers and Amazon SageMaker

You can deploy GPT-J 6B for inference using Hugging Face Transformers and Amazon SageMaker. This integration enables scalable and secure model deployment. Builders can use SageMaker's managed infrastructure for hosting GPT-J 6B. The deployment process leverages Hugging Face's Transformers library for model optimization and SageMaker's capabilities for scalable and secure hosting.

Key takeaways
  • GPT-J 6B deployable on SageMaker via Hugging Face Transformers.
  • SageMaker provides managed infrastructure for scalable and secure hosting.
  • Hugging Face Transformers library used for model optimization.
HHugging Face Blog#model-deployment#cloud-inference#transformers
otherJul 8

Deploy Hugging Face models easily with Amazon SageMaker

Hugging Face and Amazon SageMaker have integrated to simplify model deployment for builders. This integration allows users to deploy Hugging Face models directly on SageMaker, streamlining workflows and reducing operational overhead. You can now easily deploy and manage models on SageMaker. The integration supports popular Hugging Face model repositories.

Key takeaways
  • Hugging Face models deployable on Amazon SageMaker directly.
  • Integration streamlines workflows and reduces operational overhead.
  • Supports popular Hugging Face model repositories.
HHugging Face Blog#model-deployment#cloud-ai#hugging-face
modelsJun 3

Few-shot learning in practice: GPT-Neo and the 🤗 Accelerated Inference API

The Hugging Face blog post explores few-shot learning with GPT-Neo and their Accelerated Inference API. Few-shot learning enables models to learn from a small number of examples. The post demonstrates how to use the API for fast and efficient model deployment. You can apply these techniques to improve model performance on limited-data tasks.

Key takeaways
  • Few-shot learning uses a small number of examples to adapt models.
  • Hugging Face provides an Accelerated Inference API for efficient deployment.
  • GPT-Neo is a model suitable for few-shot learning tasks.
HHugging Face Blog#few-shot-learning#inference-api#model-deployment