Is the training Qualiopi certified?

Yes, Learni is a Qualiopi-certified training organization. This certification guarantees the quality of our courses and enables funding through OPCO and other mechanisms.

How can I fund my training?

Our courses are eligible for OPCO funding. Learni supports you through the funding process. A personalized quote is available upon request.

What are the prerequisites for this training?

The prerequisites for the Training Hugging Face Hub - Sharing and Deploying AI Models training are: Mastery of Python, basics in PyTorch or TensorFlow, and pre-trained models. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training Hugging Face Hub - Sharing and Deploying AI Models training is available in distanciel. The format can be adapted to your needs (inter, intra, custom).

Training Hugging Face Hub - Sharing and Deployin... | Learni

The story of Learni

Founded by passionate advocates of learning and innovation, Learni set out to make professional training accessible to everyone, everywhere in the world. Our team works in the largest cities such as Paris, Lyon, Marseille, and internationally, to support talents and organizations in their skills development.

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Methods, approaches, and teaching resources

The Training Hugging Face Hub - Sharing and Deploying AI Models training is delivered in-person or remotely (blended-learning, e-learning, virtual classroom, remote in-person). At Learni, a Qualiopi-certified training organization, each program is designed to maximize skills acquisition, regardless of the training mode chosen.

The trainer alternates between demonstrative, interrogative, and active methods (through practical exercises and/or real-world scenarios). This pedagogical approach ensures concrete and directly applicable learning in the workplace.

Teaching resources provided

To ensure the quality of the Training Hugging Face Hub - Sharing and Deploying AI Models training, Learni provides the following teaching resources:

Mac or PC computers, high-speed fiber internet, whiteboard or flipchart, video projector or interactive touchscreen (for remote sessions)
Training environments installed on workstations or accessible online
Course materials, practical exercises, and supplementary resources
Post-training access to materials and teaching resources

For in-house training at a location external to Learni, the client ensures and commits to having all necessary teaching materials (IT equipment, internet connection...) for the proper conduct of the training action in accordance with the prerequisites indicated in the communicated training program.

* contact us for remote feasibility** ratio varies depending on the training followed

Assessment methods

The assessment of skills acquired during the Training Hugging Face Hub - Sharing and Deploying AI Models training is carried out through:

During training: case studies, practical exercises, and professional scenarios
At the end of training: self-assessment questionnaire and skills evaluation by the trainer
After training: training completion certificate detailing acquired skills

Training accessibility

Learni is committed to the accessibility of its professional training programs. All our training programs are accessible to people with disabilities. Our teams are available to adapt teaching methods to your specific needs. Do not hesitate to contact us for any accommodation request.

Training objectives

Master Hugging Face Hub for managing professional AI models

Develop skills in uploading and versioning models in a corporate environment

Implement interactive Spaces to demonstrate AI applications

Optimize ML pipeline deployment via the certified Hub

Design collaborative workflows for data teams

Integrate the Hub into scalable production projects

Training program

Module 1Hugging Face Hub Fundamentals: Exploring the Library and Installing the Ecosystem (Transformers, Datasets, Tokenizers)

Dive into the Hugging Face Hub interface to search and download thousands of pre-trained models suited to your professional needs, install essential libraries like Transformers and Datasets via pip, perform your first tests on common NLP tasks such as text classification or generation, complete practical exercises on real company datasets, produce a comparative model evaluation report to identify the best performing ones.

Module 2Model Management on Hugging Face Hub: Upload, Versioning, and Metadata (Git LFS, Model Cards)

Learn to upload your fine-tuned models to Hugging Face Hub using Git and LFS for smooth handling of large files, configure detailed Model Cards with benchmarks and use cases, version your iterations for impeccable professional tracking, test integration with corporate CI/CD pipelines, develop a concrete case of uploading a customized BERT model, generate deliverables like a Hub repository ready for team sharing.

Module 3Hugging Face Hub Spaces: Creating Interactive Demos and Gradio/Streamlit Apps

Build dynamic Spaces on Hugging Face Hub to host interactive AI applications without a dedicated server, integrate Gradio or Streamlit for intuitive user interfaces, deploy a demo of image recognition or chatbots live, optimize GPU sharing for scalable performance, collaborate in real-time via forks and PRs, produce a fully functional complete Space as a final deliverable, ready for client or internal presentations.

Module 4Advanced Hugging Face Hub: Collaborative Pipelines, Security, and Production Deployment (Inference API, Private Repos)

Master private repositories to secure sensitive corporate models on Hugging Face Hub, configure the Inference API for fast serverless inferences, integrate collaborative workflows with organizations and teams, optimize costs and scalability for production, address advanced use cases like distributed fine-tuning, deploy an end-to-end pipeline with monitoring, conclude with a certifying ongoing project to validate your new professional skills.