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 ONNX Runtime - Deploying High-Performance AI Models training are: Python basics, Machine Learning fundamentals, and handling simple models. A preliminary interview validates your eligibility.

Is the training available in person?

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

Training ONNX Runtime - Deploying… | 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 - Deploying High-Performance 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 - Deploying High-Performance 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 - Deploying High-Performance 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 the fundamentals of ONNX Runtime for professional AI inferences

Develop skills in converting and optimizing models to ONNX format

Implement fast inference execution with ONNX Runtime in enterprise environments

Configure optimized sessions for CPU, GPU, and edge environments

Design scalable, certified deployments of AI applications

Optimize performance for professional, responsive ML projects

Training program

Module 1ONNX Runtime Fundamentals: Installation and First Models (Python, TensorFlow/PyTorch Conversion)

Complete discovery of the ONNX Runtime environment with quick installation via pip and conda, practical conversion of popular TensorFlow or PyTorch models to the universal ONNX format, creation of basic inference sessions on concrete datasets like MNIST or CIFAR, guided exercises to load models, run predictions, and visualize results, initial performance benchmarks to identify immediate gains in your professional workflows, all accompanied by a personal capstone project to anchor skills from the first day.

Module 2ONNX Runtime Optimization: Inference Acceleration (Quantization, Graph Fusion, CPU/GPU Providers)

Dive into essential optimization techniques with dynamic and static quantization to reduce model size by up to 4x, operator graph fusion to boost execution speed, configuration of providers like CPUExecutionProvider and CUDA to fully leverage available hardware, practical workshops on real enterprise cases involving image classification or NLP, precise measurement of gains in latency and throughput using built-in tools, iterative adjustments on your capstone project for production-ready performance, demonstrating immediate value for efficient AI deployments.

Module 3ONNX Runtime Deployment: Production Integration (Web Services, Edge Devices, Monitoring)

Master practical deployment with ONNX Runtime integration into Flask/FastAPI APIs for scalable web services, packaging for edge computing on Raspberry Pi or mobiles, setting up real-time performance monitoring with logs and metrics, exercises on Docker containerization for total portability, end-to-end tests on critical business scenarios like speech recognition or anomaly detection, finalization and presentation of your optimized capstone project, delivery of ready-to-use deliverables to boost your certified professional AI skills.