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 Optimized AI Models training are: Python basics and machine learning fundamentals. A preliminary interview validates your eligibility.

Is the training available in person?

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

Training ONNX Runtime - Deploying Optimized AI M... | 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 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 - Deploying 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 - Deploying 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 for professional deployments

Develop skills in inference of optimized AI models

Implement ONNX Runtime in certified enterprise environments

Configure high-performance execution sessions with ONNX Runtime

Optimize latency and memory for scalable AI applications

Integrate ONNX Runtime into production ML pipelines

Produce enterprise-ready deliverables

Training program

Module 1ONNX Runtime Fundamentals: Installation and First Models (Python, Conversion Tools)

Discover the quick installation of ONNX Runtime via pip and conda, conversion of TensorFlow or PyTorch models to ONNX with practical exercises on real datasets like MNIST, creation of simple inference sessions, execution of batch and synchronous predictions, analysis of logs to identify first optimizations, production of a first professional deployable script for enterprise use, with personalized code review by the trainer to validate your skills from day one.

Module 2Advanced ONNX Runtime Execution: Provider Management and Optimization (CPU, GPU, Providers)

Dive into configuring CPU and CUDA providers to accelerate inference, manipulate ONNX graphs with tools like Netron to visualize and optimize, implement operator fusion techniques and post-training quantization on concrete business cases, test memory management with large datasets, measure performance gains via integrated benchmarks, generate optimization reports ready for your DevOps teams, transform slow models into ultra-high-performance solutions in a single practical workshop.

Module 3ONNX Runtime Integration and Deployment: Real Applications and Production (Docker, APIs)

Integrate ONNX Runtime into web applications via Flask and FastAPI with exercises on REST inference services, containerize models with Docker for smooth cloud deployment, simulate enterprise scenarios like real-time object detection, secure endpoints with basic authentication, monitor live performance with Prometheus, produce a complete and deployable ongoing project, receive a certified action plan to scale in production and immediately boost your professional AI skills.