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: Mastering Hugging Face Hub - Deployment and Management of AI Models training are: Basic knowledge of machine learning and Python, fundamental concepts of AI models. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training: Mastering Hugging Face Hub - Deployment and Management of AI Models training is available in remote. The format can be adapted to your needs (inter, intra, custom).

Training: Mastering Hugging Face Hub - Deploymen... | Learni

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Methods, approaches, and teaching resources

The Training: Mastering Hugging Face Hub - Deployment and Management of 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: Mastering Hugging Face Hub - Deployment and Management of 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: Mastering Hugging Face Hub - Deployment and Management of 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

Understand the Hugging Face ecosystem and the role of the Hub

Use and integrate pre-trained models via the Hub

Publish, document, and manage custom models on the Hub

Leverage data management, datasets, and Spaces for distributing interactive AI applications

Set up collaborative and reproducible workflows for R&D

Deploy AI models in production via Hugging Face tools

Training program

Module 1Introduction to Hugging Face Hub and Model Usage

Introduction to the Hugging Face ecosystem, account registration and management, exploration of the Hub and its resources (models, datasets, Spaces). Searching, downloading, and integrating pre-trained models into a Python project using transformers, using task pipelines (NLP, vision, audio), and hands-on practice on common use cases (classification, text generation, etc.).

Module 2Publication, Collaborative Management, and Best Practices

Creating a repository, uploading and versioning personal models using CLI and API, managing metadata, documentation (README, model cards). Sharing models with the community, fine-grained access control (public, private, organization), change tracking. Reproducibility concepts (model cards, config files) and CI/CD integration for automated model testing. Introduction to dataset management and publication on the Datasets Hub.

Module 3Deploying and Distributing Models: Spaces, Applications, and Production

Overview of Spaces: hosting interactive applications (Streamlit, Gradio) with end-to-end demo. Designing a mini AI application deployed on Space, deployment management, monitoring, authentication. Strategies for deploying models in production on Hugging Face Inference Endpoints. Discussion on team workflows, collaboration via Git and organizations, automating publications, and managing the model lifecycle in production.

Evaluation method

Multiple-choice quiz at the end of each day
Practical workshops covering all Hub features
Red thread project: creation, publication, and distribution of a custom AI model on Hugging Face Hub