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 - Deploy High-Performance AI Models training are: Python basics, elementary machine learning concepts, and Linux/Windows environment. A preliminary interview validates your eligibility.

Is the training available in person?

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

Training ONNX Runtime - Deploy High-Performance… | 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 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 - Deploy 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 - Deploy 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 installation of ONNX Runtime for professional enterprise deployments

Understand the ONNX format and develop skills in model interoperability

Implement AI model inference with certifying unit tests

Optimize ONNX Runtime performance via various backends in practical training

Integrate ONNX Runtime into CI/CD pipelines for enhanced quality assurance

Design automated functional tests on models exported to ONNX

Training program

Module 1Introduction to ONNX Runtime: installation and first unit tests (Python, basic tools)

Discover ONNX Runtime through a guided installation on Windows and Linux, configure essential dependencies like pip and conda, perform your first unit tests on simple models imported from TensorFlow or PyTorch, validate interoperability with timed practical exercises, produce initial validation reports to ensure a solid foundation in software quality assurance.

Module 2ONNX Format: conversion and functional tests (model export, validation tools)

Convert PyTorch or TensorFlow models to ONNX format using onnx.converter, test validity via onnx.checker and visualization tools like Netron, implement automated functional tests to verify identical predictions before/after conversion, analyze common errors with interactive debugging, generate certifying deliverables for integration into professional QA pipelines.

Module 3ONNX Runtime Inference: CPU/GPU backends and performance tests (OrtSession, benchmarks)

Create inference sessions with OrtSession on CPU and CUDA backends, measure performance via built-in benchmarks and tools like ONNX Runtime perf, develop load tests to simulate production, optimize model graphs with operator fusion, produce quantified performance reports, apply real business cases for rapid upskilling in AI software testing.

Module 4ONNX Runtime Optimization: graph tuning and advanced QA tests (transformers, quantization)

Apply graph optimizations with onnxruntime-tools, test INT8 quantization for speed gains up to 4x, integrate transformers like BERT with end-to-end functional tests, measure impact on accuracy and latency via standard QA metrics, debug with integrated profilers, deliver optimized models ready for CI/CD deployment, strengthening your professionally certifying skills.

Module 5ONNX Runtime Integration: CI/CD and full production tests (Docker, Jenkins)

Integrate ONNX Runtime into Docker containers for scalable deployments, automate unit and functional tests in Jenkins or GitHub Actions pipelines, simulate production environments with variable loads, validate robustness via basic chaos engineering, generate monitoring dashboards with Prometheus, conclude with a capstone project certifying your new skills in corporate quality assurance.