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.

What is the duration of the Training ONNX Runtime - Deploy Optimized AI Models training?

The Training ONNX Runtime - Deploy Optimized AI Models training takes place over 3 journées. The program is structured in progressive modules for optimal skill development.

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 ONNX Runtime - Deploy Optimized AI Models training are: Python basics and introductory machine learning concepts. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training ONNX Runtime - Deploy Optimized AI Models training is available in distanciel. The format can be adapted to your needs (inter, intra, custom).

Training ONNX Runtime - Deploy Optimized AI Models | 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 ONNX Runtime - Deploy Optimized 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 ONNX Runtime - Deploy Optimized 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 ONNX Runtime - Deploy Optimized 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 access terms and deadlines

Training objectives

Master the fundamentals of ONNX Runtime to deploy professional AI models

Develop skills in converting and executing ONNX models in an enterprise environment

Implement optimized inference with ONNX Runtime across various environments

Configure integration pipelines for certified AI applications

Optimize inference performance for scalable professional projects

Design cross-platform deployments with ONNX Runtime in production

Training program

Module 1Fundamentals: Install ONNX Runtime and run first models (Python, conversion tools)

Quick installation of ONNX Runtime in a dedicated Python environment, conversion of TensorFlow or PyTorch models to ONNX format using official tools, execution of simple inferences on real datasets, practical exercises to load and predict with your own models, analysis of outputs to validate results, creation of a first professional script ready for enterprise use, with guided debugging by the trainer.

Module 2Optimization: Accelerate ONNX Runtime inference (CPU/GPU providers, quantization)

Exploration of ONNX Runtime execution providers for CPU and NVIDIA GPU, application of quantization techniques to reduce model size without loss of precision, optimization of computation graphs via operator fusion, comparative performance tests on concrete business cases, integration into performant Python scripts, production of personal benchmarks, iterative adjustments for measurable gains up to 5x faster.

Module 3Deployment: Integrate ONNX Runtime in production (API, containers, monitoring)

Development of a REST API with Flask to serve ONNX Runtime models, containerization via Docker for scalable deployments, configuration of real-time inference performance monitoring, exercises on AWS or Azure cloud integration, practical business cases with simulated traffic, finalization of a deployable ongoing project, delivery of optimized source code and certified production deployment plan.