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 - Accelerate ML Inference in Production training are: Knowledge of Python, basics in Machine Learning, and handling of ONNX models. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training ONNX Runtime - Accelerate ML Inference in Production training is available in distanciel. The format can be adapted to your needs (inter, intra, custom).

Training ONNX Runtime - Accelerate ML Inference… | 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 - Accelerate ML Inference in Production 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 - Accelerate ML Inference in Production 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 - Accelerate ML Inference in Production 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 ONNX Runtime to deploy professional ML models in production

Optimize model inference with ONNX Runtime for maximum performance

Develop cross-platform applications integrating ONNX Runtime in the enterprise

Implement hardware acceleration techniques with certified ONNX Runtime

Design scalable and secure inference pipelines

Evaluate and benchmark ONNX Runtime performance in real-world contexts

Acquire certifying skills in ONNX Runtime to boost your career

Training program

Module 1ONNX Runtime Fundamentals: Installation and First Models (Python, C++ Tools, Initial Benchmarks)

Quick installation of ONNX Runtime on various environments, hands-on with Python and C++ APIs to load your first ONNX models, execution of basic inferences on real datasets like ImageNet, practical exercises to convert TensorFlow or PyTorch models to ONNX, analysis of performance logs and creation of your first professional inference script, with code review by the trainer to validate enterprise best practices.

Module 2ONNX Runtime Optimization: Model Tuning and CPU/GPU Acceleration (Providers, Graph Optimizations)

Exploration of ONNX Runtime providers for CPU, GPU CUDA, and DirectML, application of automatic and manual graph optimizations on your models, exercises to reduce inference latency by 50% on concrete enterprise cases, use of tools like ONNX Optimizer and Model Profiler, development of custom scripts for quantization and pruning, comparative tests before/after with precise metrics, deliverable: optimized model ready for production.

Module 3ONNX Runtime Integration: Developing Real Applications (Web Services, Mobile, Edge Computing)

Integration of ONNX Runtime into Python Flask applications for inference APIs, development of a scalable web service with session management and batching, exercises on edge deployment with ONNX Runtime Mobile for Android/iOS, connection to databases for end-to-end pipelines, practical cases inspired by production such as real-time image recognition, advanced debugging and monitoring with integrated tools, production of a functional prototype usable immediately in the enterprise.

Module 4ONNX Runtime Production Deployment: Scaling, Security, and Monitoring (Docker, Kubernetes, Observability)

Containerization of models with Docker and official ONNX Runtime containers, orchestration with Kubernetes for auto-scaling, implementation of security (authentication, input encryption), setup of monitoring with Prometheus and Grafana for latency/throughput, exercises on error handling and rollbacks in production, multi-hardware benchmarks and final optimization, deliverable: complete deployment pipeline with monitoring dashboard, ready for your critical business challenges.

Evaluation method

Multiple-choice quiz to validate acquired knowledge at the end of the training
Continuous evaluation through practical exercises and benchmarks
Presentation of the ONNX Runtime red thread project to the trainer

Learning method

Courses led by an active ONNX Runtime expert trainer
Practical exercises on real enterprise ML cases
Progressive red thread project for an optimized production model
Complete course materials and ONNX Runtime resources provided to each participant